22 Experimental Design

Imagine you’re a marketing manager trying to decide whether a new advertising campaign will boost sales. Or perhaps you’re an economist investigating the impact of tax incentives on consumer spending. In both cases, you need a way to determine causal effects—not just correlations. This is where experimental design becomes essential.

At its core, experimental design ensures that studies are conducted efficiently and that valid conclusions can be drawn. In business applications, experiments help quantify the effects of pricing strategies, marketing tactics, and financial interventions. A well-designed experiment reduces bias, controls variability, and maximizes the accuracy of causal inferences.

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22.1 Principles of Experimental Design

A well-designed experiment follows three key principles:

1. Randomization: Ensures that subjects or experimental units are assigned to different groups randomly, eliminating selection bias.
2. Replication: Increases the precision of estimates by repeating the experiment on multiple subjects.
3. Control: Isolates the effect of treatments by using control groups or baseline conditions to minimize confounding factors.

In addition to these, blocking and factorial designs further refine experimental accuracy.

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22.2 The Gold Standard: Randomized Controlled Trials

Randomized Controlled Trials (RCTs) are the holy grail of causal inference. Their power comes from random assignment, which ensures that any differences between treatment and control groups arise only due to the intervention.

RCTs provide:

• Unbiased estimates of treatment effects
• Elimination of confounding factors on average (although covariate imbalance can occur, necessitating techniques like [Rerandomization] to achieve a “platinum standard” set by Tukey (1993))

An RCT consists of two groups:

1. Treatment group: Receives the intervention (e.g., a new marketing campaign, drug, or financial incentive).
2. Control group: Does not receive the intervention, serving as a baseline.

Subjects from the same population are randomly assigned to either group. This randomization ensures that any observed differences in outcomes are due solely to the treatment—not external factors.

However, RCTs are easier to conduct in hard sciences (e.g., medicine or physics), where treatments and environments can be tightly controlled. In social sciences, challenges arise because:

• Human behavior is unpredictable.
• Some treatments are impossible or unethical to introduce (e.g., assigning individuals to poverty).
• Real-world environments are difficult to control.

To address these challenges, social scientists use Quasi-Experimental Methods to approximate experimental conditions.

RCTs establish internal validity, meaning that the observed treatment effects are causally interpretable. Even though random assignment is not the same as ceteris paribus (holding everything else constant), it achieves a similar effect: on average, treatment and control groups should be comparable.

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22.3 Selection Problem

A fundamental challenge in causal inference is that we never observe both potential outcomes for the same individual—only one or the other. This creates the selection problem, which we formalize below.

Assume we have:

• A binary treatment variable:
  $D_i \in \{0,1\}$, where:
  • $D_i = 1$ indicates that individual $i$ receives the treatment.
  • $D_i = 0$ indicates that individual $i$ does not receive the treatment.
• The outcome of interest:
  $Y_i$, which depends on whether the individual is treated or not:
  • $Y_{0i}$: The outcome if not treated.
  • $Y_{1i}$: The outcome if treated.

Thus, the potential outcomes framework is defined as:

$$\text{Potential Outcome} = \begin{cases} Y_{1i}, & \text{if } D_i = 1 \quad (\text{Treated}) \\ Y_{0i}, & \text{if } D_i = 0 \quad (\text{Untreated}) \end{cases}$$

However, we only observe one outcome per individual:

$$Y_i = Y_{0i} + (Y_{1i} - Y_{0i})D_i$$

This means that for any given person, we either observe $Y_{1i}$ or $Y_{0i}$, but never both. Since we cannot observe counterfactuals (unless we invent a time machine), we must rely on statistical inference to estimate treatment effects.

22.3.1 The Observed Difference in Outcomes

The goal is to estimate the difference in expected outcomes between treated and untreated individuals:

$$E[Y_i | D_i = 1] - E[Y_i | D_i = 0]$$

Expanding this equation:

$$\begin{aligned} E[Y_i | D_i = 1] - E[Y_i | D_i = 0] &= (E[Y_{1i} | D_i = 1] - E[Y_{0i}|D_i = 1] ) \\ &+ (E[Y_{0i} |D_i = 1] - E[Y_{0i} |D_i = 0]) \\ &= (E[Y_{1i}-Y_{0i}|D_i = 1] ) \\ &+ (E[Y_{0i} |D_i = 1] - E[Y_{0i} |D_i = 0]) \end{aligned}$$

This equation decomposes the observed difference into two components:

• Treatment Effect on the Treated: $E[Y_{1i} - Y_{0i} |D_i = 1]$, which represents the causal impact of the treatment on those who are treated.

• Selection Bias:
  $E[Y_{0i} |D_i = 1] - E[Y_{0i} |D_i = 0]$, which captures systematic differences between treated and untreated groups even in the absence of treatment.

Thus, the observed difference in outcomes is:

$$\text{Observed Difference} = \text{ATT} + \text{Selection Bias}$$

22.3.2 Eliminating Selection Bias with Random Assignment

With random assignment of treatment, $D_i$ is independent of potential outcomes:

$$E[Y_i | D_i = 1] - E[Y_i|D_i = 0] = E[Y_{1i} - Y_{0i}]$$

This works because, under true randomization:

$$E[Y_{0i} | D_i = 1] = E[Y_{0i} | D_i = 0]$$

which eliminates selection bias. Consequently, the observed difference now directly estimates the true causal effect:

$$E[Y_i | D_i = 1] - E[Y_i | D_i = 0] = E[Y_{1i} - Y_{0i}]$$

Thus, randomized controlled trials provide an unbiased estimate of the average treatment effect.

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22.3.3 Another Representation Under Regression

So far, we have framed the selection problem using expectations and potential outcomes. Another way to represent treatment effects is through regression models, which provide a practical framework for estimation.

Suppose the treatment effect is constant across individuals:

$$Y_{1i} - Y_{0i} = \rho$$

This implies that each treated individual experiences the same treatment effect ($\rho$), though their baseline outcomes ($Y_{0i}$) may vary.

Since we only observe one of the potential outcomes, the observed outcome can be expressed as:

$$\begin{aligned} Y_i &= E(Y_{0i}) + (Y_{1i} - Y_{0i}) D_i + [Y_{0i} - E(Y_{0i})] \\ &= \alpha + \rho D_i + \eta_i \end{aligned}$$

where:

• $\alpha = E(Y_{0i})$, the expected outcome for untreated individuals.
• $\rho$ represents the causal treatment effect.
• $\eta_i = Y_{0i} - E(Y_{0i})$, capturing individual deviations from the mean untreated outcome.

Thus, the regression model provides an intuitive way to express treatment effects.

22.3.3.1 Conditional Expectations and Selection Bias

Taking expectations conditional on treatment status:

$$\begin{aligned} E[Y_i |D_i = 1] &= \alpha + \rho + E[\eta_i |D_i = 1] \\ E[Y_i |D_i = 0] &= \alpha + E[\eta_i |D_i = 0] \end{aligned}$$

The observed difference in means between treated and untreated groups is:

$$E[Y_i |D_i = 1] - E[Y_i |D_i = 0] = \rho + E[\eta_i |D_i = 1] - E[\eta_i |D_i = 0]$$

Here, the term $E[\eta_i |D_i = 1] -E[\eta_i |D_i = 0]$ represents selection bias—the correlation between the regression error term ($\eta_i$) and the treatment variable ($D_i$).

Under random assignment, we assume that potential outcomes are independent of treatment ($D_i$):

$$E[\eta_i |D_i = 1] -E[\eta_i |D_i = 0] = E[Y_{0i} |D_i = 1] -E[Y_{0i}|D_i = 0] = 0$$

Thus, under true randomization, selection bias disappears, and the observed difference directly estimates the causal effect $\rho$.

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22.3.3.2 Controlling for Additional Variables

In many real-world scenarios, random assignment is imperfect, and selection bias may still exist. To mitigate this, we introduce control variables ($X_i$), such as demographic characteristics, firm size, or prior purchasing behavior.

If $X_i$ is uncorrelated with treatment ($D_i$), including it in our regression model does not bias the estimate of $\rho$ but has two advantages:

1. It reduces residual variance ($\eta_i$), improving the precision of $\rho$.
2. It accounts for additional sources of variability, making the model more robust.

Thus, our regression model extends to:

$$Y_i = \alpha + \rho D_i + X_i'\gamma + \eta_i$$

where:

• $X_i$ represents a vector of control variables.
• $\gamma$ captures the effect of $X_i$ on the outcome.

22.3.3.3 Example: Racial Discrimination in Hiring

A famous study by Bertrand and Mullainathan (2004) examined racial discrimination in hiring by randomly assigning Black- and White-sounding names to identical job applications. By ensuring that names were assigned randomly, the authors eliminated confounding factors like education and experience, allowing them to estimate the causal effect of race on callback rates.

22.4 Classical Experimental Designs

Experimental designs provide structured frameworks for conducting experiments, ensuring that results are statistically valid and practically applicable. The choice of design depends on the research question, the nature of the treatment, and potential sources of variability. For a more in-depth statistical understanding of these designs, we will revisit them again in Analysis of Variance.

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22.4.1 Completely Randomized Design

In a Completely Randomized Design (CRD), each experimental unit is randomly assigned to a treatment group. This is the simplest form of experimental design and is effective when no confounding factors are present.

Example: Email Marketing Experiment

A company tests three different email marketing strategies (A, B, and C) to measure their effect on customer engagement (click-through rate). Customers are randomly assigned to receive one of the three emails.

Mathematical Model

$$Y_{ij} = \mu + \tau_i + \epsilon_{ij}$$

where:

• $Y_{ij}$ is the response variable (e.g., click-through rate).
• $\mu$ is the overall mean response.
• $\tau_i$ is the effect of treatment $i$.
• $\epsilon_{ij}$ is the random error term, assumed to be normally distributed: $\epsilon_{ij} \sim N(0, \sigma^2)$.

set.seed(123)

# Simulated dataset for email marketing experiment
data <- data.frame(
  group = rep(c("A", "B", "C"), each = 10),
  response = c(rnorm(10, mean=50, sd=5),
               rnorm(10, mean=55, sd=5),
               rnorm(10, mean=60, sd=5))
)

# ANOVA to test for differences among groups
anova_result <- aov(response ~ group, data = data)
summary(anova_result)
#>             Df Sum Sq Mean Sq F value  Pr(>F)   
#> group        2  306.1  153.04   6.435 0.00518 **
#> Residuals   27  642.1   23.78                   
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

If the p-value in the ANOVA summary is less than 0.05, we reject the null hypothesis and conclude that at least one email strategy significantly affects engagement.

22.4.2 Randomized Block Design

A Randomized Block Design (RBD) is used when experimental units can be grouped into homogeneous blocks based on a known confounding factor. Blocking helps reduce unwanted variation, increasing the precision of estimated treatment effects.

Example: Store Layout Experiment

A retailer tests three store layouts (A, B, and C) on sales performance. Since store location (Urban, Suburban, Rural) might influence sales, we use blocking to control for this effect.

Mathematical Model $$Y_{ij} = \mu + \tau_i + \beta_j + \epsilon_{ij}$$ where:

• $Y_{ij}$ is the sales outcome for store $i$ in location $j$.

• $\mu$ is the overall mean sales.

• $\tau_i$ is the effect of layout $i$.

• $\beta_j$ represents the block effect (location).

• $\epsilon_{ij}$ is the random error.

set.seed(123)

# Simulated dataset for store layout experiment
data <- data.frame(
  block = rep(c("Urban", "Suburban", "Rural"), each = 6),
  layout = rep(c("A", "B", "C"), times = 6),
  sales = c(rnorm(6, mean=200, sd=20),
            rnorm(6, mean=220, sd=20),
            rnorm(6, mean=210, sd=20))
)

# ANOVA with blocking factor
anova_block <- aov(sales ~ layout + block, data = data)
summary(anova_block)
#>             Df Sum Sq Mean Sq F value Pr(>F)
#> layout       2     71    35.7   0.071  0.931
#> block        2    328   164.1   0.328  0.726
#> Residuals   13   6500   500.0

By including block in the model, we account for location effects, leading to more accurate treatment comparisons.

22.4.3 Factorial Design

A Factorial Design evaluates the effects of two or more factors simultaneously, allowing for the study of interactions between variables.

Example: Pricing and Advertising Experiment

A company tests two pricing strategies (High, Low) and two advertising methods (TV, Social Media) on sales.

Mathematical Model $$Y_{ijk} = \mu + \tau_i + \gamma_j + (\tau\gamma)_{ij} + \epsilon_{ijk}$$

where:

• $\tau_i$ is the effect of price level $i$.

• $\gamma_j$ is the effect of advertising method $j$.

• $(\tau\gamma)_{ij}$ is the interaction effect between price and advertising.

• $\epsilon_{ijk}$ is the random error term.

set.seed(123)

# Simulated dataset
data <- expand.grid(
  Price = c("High", "Low"),
  Advertising = c("TV", "Social Media"),
  Replicate = 1:10
)

# Generate response variable (sales)
data$Sales <- with(data, 
                   100 + 
                   ifelse(Price == "Low", 10, 0) + 
                   ifelse(Advertising == "Social Media", 15, 0) + 
                   ifelse(Price == "Low" & Advertising == "Social Media", 5, 0) +
                   rnorm(nrow(data), sd=5))

# Two-way ANOVA
anova_factorial <- aov(Sales ~ Price * Advertising, data = data)
summary(anova_factorial)
#>                   Df Sum Sq Mean Sq F value   Pr(>F)    
#> Price              1   1364    1364   66.60 1.05e-09 ***
#> Advertising        1   3640    3640  177.67 1.72e-15 ***
#> Price:Advertising  1     15      15    0.71    0.405    
#> Residuals         36    738      20                     
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

22.4.4 Crossover Design

A Crossover Design is used when each subject receives multiple treatments in a sequential manner. This design controls for individual differences by using each subject as their own control.

Example: Drug Trial

Patients receive Drug A in the first period and Drug B in the second period, or vice versa.

Mathematical Model $$Y_{ijk} = \mu + \tau_i + \pi_j + \beta_k + \epsilon_{ijk}$$ where:

• $\tau_i$ is the treatment effect.

• $\pi_j$ is the period effect (e.g., learning effects).

• $\beta_k$ is the subject effect (individual baseline differences).

set.seed(123)

# Simulated dataset
data <- data.frame(
  Subject = rep(1:10, each = 2),
  Period = rep(c("Period 1", "Period 2"), times = 10),
  Treatment = rep(c("A", "B"), each = 10),
  Response = c(rnorm(10, mean=50, sd=5), rnorm(10, mean=55, sd=5))
)

# Crossover ANOVA
anova_crossover <-
  aov(Response ~ Treatment + Period + Error(Subject / Period),
      data = data)
summary(anova_crossover)
#> 
#> Error: Subject
#>           Df Sum Sq Mean Sq
#> Treatment  1  63.94   63.94
#> 
#> Error: Subject:Period
#>        Df Sum Sq Mean Sq
#> Period  1  21.92   21.92
#> 
#> Error: Within
#>           Df Sum Sq Mean Sq F value Pr(>F)  
#> Treatment  1  134.9  134.91   5.231 0.0371 *
#> Period     1    0.2    0.24   0.009 0.9237  
#> Residuals 15  386.9   25.79                 
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

22.4.5 Split-Plot Design

A Split-Plot Design is used when one factor is applied at the group (whole-plot) level and another at the individual (sub-plot) level. This design is particularly useful when some factors are harder or more expensive to randomize than others.

Example: Farming Experiment

A farm is testing two irrigation methods (Drip vs. Sprinkler) and two soil types (Clay vs. Sand) on crop yield. Since irrigation systems are installed at the farm level and are difficult to change, they are treated as the whole-plot factor. However, different soil types exist within each farm and can be tested more easily, making them the sub-plot factor.

---

Mathematical Model

The statistical model for a Split-Plot Design is:

$$Y_{ijk} = \mu + \alpha_i + B_k + \beta_j + (\alpha\beta)_{ij} + \epsilon_{ijk}$$

where:

• $Y_{ijk}$ is the response (e.g., crop yield).
  
• $\mu$ is the overall mean.
  
• $\alpha_i$ is the whole-plot factor (Irrigation method).
  
• $B_k$ is the random block effect (Farm-level variation).
  
• $\beta_j$ is the sub-plot factor (Soil type).
  
• $(\alpha\beta)_{ij}$ is the interaction effect between Irrigation and Soil type.
  
• $\epsilon_{ijk} \sim N(0, \sigma^2)$ represents the random error term.

The key feature of the Split-Plot Design is that the whole-plot factor ($\alpha_i$) is tested against the farm-level variance ($B_k$), while the sub-plot factor ($\beta_j$) is tested against individual variance ($\epsilon_{ijk}$).

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We model the Split-Plot Design using a Mixed Effects Model, treating Farm as a random effect to account for variation at the whole-plot level.

set.seed(123)

# Simulated dataset for a split-plot experiment
data <- data.frame(
  Farm = rep(1:6, each = 4), # 6 farms (whole plots)
  
  # Whole-plot factor
  Irrigation = rep(c("Drip", "Sprinkler"), each = 12), 
  Soil = rep(c("Clay", "Sand"), times = 12), # Sub-plot factor
  
  # Response variable
  Yield = c(rnorm(12, mean=30, sd=5), rnorm(12, mean=35, sd=5)) 
)

# Load mixed-effects model library
library(lme4)

# Mixed-effects model: Whole-plot factor (Irrigation) as a random effect
model_split <- lmer(Yield ~ Irrigation * Soil + (1 | Farm), data = data)

# Summary of the model
summary(model_split)
#> Linear mixed model fit by REML ['lmerMod']
#> Formula: Yield ~ Irrigation * Soil + (1 | Farm)
#>    Data: data
#> 
#> REML criterion at convergence: 128.1
#> 
#> Scaled residuals: 
#>      Min       1Q   Median       3Q      Max 
#> -1.72562 -0.57572 -0.09767  0.60248  2.04346 
#> 
#> Random effects:
#>  Groups   Name        Variance Std.Dev.
#>  Farm     (Intercept)  0.00    0.000   
#>  Residual             24.79    4.979   
#> Number of obs: 24, groups:  Farm, 6
#> 
#> Fixed effects:
#>                              Estimate Std. Error t value
#> (Intercept)                    31.771      2.033  15.629
#> IrrigationSprinkler             2.354      2.875   0.819
#> SoilSand                       -1.601      2.875  -0.557
#> IrrigationSprinkler:SoilSand    1.235      4.066   0.304
#> 
#> Correlation of Fixed Effects:
#>             (Intr) IrrgtS SolSnd
#> IrrgtnSprnk -0.707              
#> SoilSand    -0.707  0.500       
#> IrrgtnSp:SS  0.500 -0.707 -0.707
#> optimizer (nloptwrap) convergence code: 0 (OK)
#> boundary (singular) fit: see help('isSingular')

In this model:

• Irrigation (Whole-plot factor) is tested against Farm-level variance.

• Soil type (Sub-plot factor) is tested against Residual variance.

• The interaction between Irrigation × Soil is also evaluated.

This hierarchical structure accounts for the fact that farms are not independent, improving the precision of our estimates.

22.4.6 Latin Square Design

When two potential confounding factors exist, Latin Square Designs provide a structured way to control for these variables. This design is common in scheduling, manufacturing, and supply chain experiments.

Example: Assembly Line Experiment

A manufacturer wants to test three assembly methods (A, B, C) while controlling for work shifts and workstations. Since both shifts and workstations may influence production time, a Latin Square Design ensures that each method is tested once per shift and once per workstation.

Mathematical Model

A Latin Square Design ensures that each treatment appears exactly once in each row and column: $$Y_{ijk} = \mu + \alpha_i + \beta_j + \gamma_k + \epsilon_{ijk}$$ where:

• $Y_{ijk}$ is the outcome (e.g., assembly time).

• $\mu$ is the overall mean.

• $\alpha_i$ is the treatment effect (Assembly Method).

• $\beta_j$ is the row effect (Work Shift).

• $\gamma_k$ is the column effect (Workstation).

• $\epsilon_{ijk}$ is the random error term.

This ensures that each treatment is equally balanced across both confounding factors.

---

We implement a Latin Square Design by treating Assembly Method as the primary factor, while controlling for Shifts and Workstations.

set.seed(123)

# Define the Latin Square layout
latin_square <- data.frame(
  Shift = rep(1:3, each = 3), # Rows
  Workstation = rep(1:3, times = 3), # Columns
  
  # Treatments assigned in a balanced way
  Method = c("A", "B", "C", "C", "A", "B", "B", "C", "A"), 
  Time = c(rnorm(3, mean = 30, sd = 3),
           rnorm(3, mean = 28, sd = 3),
           rnorm(3, mean = 32, sd = 3)) # Assembly time
)

# ANOVA for Latin Square Design
anova_latin <-
  aov(Time ~ factor(Shift) + factor(Workstation) + factor(Method),
      data = latin_square)
summary(anova_latin)
#>                     Df Sum Sq Mean Sq F value Pr(>F)
#> factor(Shift)        2  1.148   0.574   0.079  0.927
#> factor(Workstation)  2 24.256  12.128   1.659  0.376
#> factor(Method)       2 14.086   7.043   0.964  0.509
#> Residuals            2 14.619   7.310

• If the p-value for “Method” is significant, we conclude that different assembly methods impact production time.

• If “Shift” or “Workstation” is significant, it indicates systematic differences across these variables.

22.5 Advanced Experimental Designs

22.5.1 Semi-Random Experiments

In semi-random experiments, participants are not fully randomized into treatment and control groups. Instead, a structured randomization process ensures fairness while allowing some level of causal inference.

22.5.1.1 Example: Loan Assignment Fairness

A bank wants to evaluate a new loan approval policy while ensuring that the experiment does not unfairly exclude specific demographics.

To maintain fairness:

1. Applicants are first stratified based on income and credit history.
2. Within each stratum, a random subset is assigned to receive the new policy, while others continue under the old policy.

set.seed(123)

# Create stratified groups
data <- data.frame(
  income_group = rep(c("Low", "Medium", "High"), each = 10),
  
  # Stratified randomization
  treatment = sample(rep(c("New Policy", "Old Policy"), each = 15)) 
)

# Display the stratification results
table(data$income_group, data$treatment)
#>         
#>          New Policy Old Policy
#>   High            6          4
#>   Low             6          4
#>   Medium          3          7

This approach ensures that each income group is fairly represented in both treatment and control conditions.

22.5.1.2 Case Study: Chicago Open Enrollment Program

A well-known example of semi-random assignment is the Chicago Open Enrollment Program (Cullen, Jacob, and Levitt 2005), where students could apply to choice schools.

However, since many schools were oversubscribed (i.e., demand exceeded supply), they used a random lottery system to allocate spots.

Thus, while enrollment itself was not fully random, the lottery outcomes were random, allowing researchers to estimate the Intent-to-Treat effect while acknowledging that not all students who won the lottery actually enrolled.

This situation presents a classic case where:

• School choice is not random: Families self-select into applying to certain schools.
• Lottery outcomes are random: Among those who apply, winning or losing the lottery is as good as a random assignment.

Let:

• $Enroll_{ij} = 1$ if student $i$ enrolls in school $j$, and $0$ otherwise.
• $Win_{ij} = 1$ if student $i$ wins the lottery, and $0$ otherwise.
• $Apply_{ij} = 1$ if student $i$ applies to school $j$.

We define:

$$\delta_j = E[Y_i | Enroll_{ij} = 1, Apply_{ij} = 1] - E[Y_i | Enroll_{ij} = 0, Apply_{ij} = 1]$$

$$\theta_j = E[Y_i | Win_{ij} = 1, Apply_{ij} = 1] - E[Y_i | Win_{ij} = 0, Apply_{ij} = 1]$$

where:

• $\delta_j$ is the treatment effect (impact of actual school enrollment).
• $\theta_j$ is the intent-to-treat effect (impact of winning the lottery).

Since not all winners enroll, we know that:

$$\delta_j \neq \theta_j$$

Thus, we can only estimate $\theta_j$ directly, and need an additional method to recover $\delta_j$.

This distinction is crucial because simply comparing lottery winners and losers does not measure the true effect of enrollment—only the effect of being given the opportunity to enroll.

To estimate the treatment effect ($\delta_j$), we use an Instrumental Variable approach:

$$\delta_j = \frac{E[Y_i | W_{ij} = 1, A_{ij} = 1] - E[Y_i | W_{ij} = 0, A_{ij} = 1]}{P(Enroll_{ij} = 1 | W_{ij} = 1, A_{ij} = 1) - P(Enroll_{ij} = 1 | W_{ij} = 0, A_{ij} = 1)}$$

where:

• $P(Enroll_{ij} = 1 | W_{ij} = 1, A_{ij} = 1)$ = probability of enrolling if winning the lottery.
• $P(Enroll_{ij} = 1 | W_{ij} = 0, A_{ij} = 1)$ = probability of enrolling if losing the lottery.

This adjustment accounts for the fact that some lottery winners do not enroll, and thus the observed effect ($\theta_j$) underestimates the true treatment effect ($\delta_j$).

This Instrumental Variable approach corrects for selection bias by leveraging randomized lottery assignment.

---

Numerical Example

Assume 10 students win the lottery and 10 students lose.

For Winners:

Type	Count	Selection Effect	Treatment Effect	Total Effect
Always Takers	1	+0.2	+1	+1.2
Compliers	2	0	+1	+1
Never Takers	7	-0.1	0	-0.1

For Losers:

Type	Count	Selection Effect	Treatment Effect	Total Effect
Always Takers	1	+0.2	+1	+1.2
Compliers	2	0	0	0
Never Takers	7	-0.1	0	-0.1

---

Computing Intent-to-Treat Effect

We compute the expected outcome for those who won and lost the lottery.

$$\begin{aligned} E[Y_i | W_{ij} = 1, A_{ij} = 1] &= \frac{1(1.2) + 2(1) + 7(-0.1)}{10} = 0.25 \\ E[Y_i | W_{ij} = 0, A_{ij} = 1] &= \frac{1(1.2) + 2(0) + 7(-0.1)}{10} = 0.05 \end{aligned}$$

Thus, the Intent-to-Treat Effect is:

$$\text{Intent-to-Treat Effect} = 0.25 - 0.05 = 0.2$$

---

Now, we calculate the probability of enrollment for lottery winners and losers:

$$\begin{aligned} P(Enroll_{ij} = 1 | W_{ij} = 1, A_{ij} = 1) &= \frac{1+2}{10} = 0.3 \\ P(Enroll_{ij} = 1 | W_{ij} = 0, A_{ij} = 1) &= \frac{1}{10} = 0.1 \end{aligned}$$

Using the formula for treatment effect ($\delta$):

$$\text{Treatment Effect} = \frac{0.2}{0.3 - 0.1} = 1$$

This confirms that the true treatment effect is 1 unit.

---

To account for additional factors ($X_i$), we extend the model as follows:

$$Y_{ia} = \delta W_{ia} + \lambda L_{ia} + X_i \theta + u_{ia}$$

where:

• $\delta$ = Intent-to-Treat effect
• $\lambda$ = True treatment effect
• $W$ = Whether a student wins the lottery
• $L$ = Whether a student enrolls in the school
• $X_i \theta$ = Control variables (i.e., reweighting of lottery), but would not affect treatment effect $E(\delta)$

Since choosing to apply to a lottery is not random, we must consider the following:

$$E(\lambda) \neq E(\lambda_1)$$

This demonstrates why lottery-based assignment is a useful but imperfect tool for causal inference—winning the lottery is random, but who applies is not.

---

22.5.2 Re-Randomization

In standard randomization, baseline covariates are only balanced on average, meaning imbalance can still occur due to random chance. Re-randomization eliminates bad randomizations by checking balance before the experiment begins (Morgan and Rubin 2012).

Key Motivations for Re-Randomization

• Randomization does not guarantee balance:
  • Example: For 10 covariates, the probability of at least one imbalance at $\alpha = 0.05$ is:
    $$1 - (1 - 0.05)^{10} = 0.40 = 40\%$$
    
  • This means a high chance of some imbalance across treatment groups.
    
• Re-randomization increases precision: If covariates correlate with the outcome, improving covariate balance improves treatment effect estimation
• Accounting for re-randomization in inference: Since re-randomization filters out bad assignments, it is equivalent to increasing the sample size and must be considered when computing standard errors.
• Alternative balancing techniques:
  • Stratified randomization (Johansson and Schultzberg 2022).
  • Matched randomization (Greevy et al. 2004; Kapelner and Krieger 2014).
  • Minimization (Pocock and Simon 1975).

Figure from USC Schaeffer Center

---

Example: Balancing Experimental Groups

An online retailer is testing two website designs (A and B) but wants to ensure that key customer demographics (e.g., age) are balanced across treatment groups.

We define a balance criterion to check if the mean age difference between groups is acceptable before proceeding.

set.seed(123)

# Define balance criterion: Ensure mean age difference < 1 year
balance_criterion <- function(data) {
  abs(mean(data$age[data$group == "A"]) - mean(data$age[data$group == "B"])) < 1
}

# Generate randomized groups, repeat until balance criterion is met
repeat {
  data <- data.frame(
    age = rnorm(100, mean = 35, sd = 10),
    group = sample(c("A", "B"), 100, replace = TRUE)
  )
  if (balance_criterion(data)) break
}

# Check final group means
tapply(data$age, data$group, mean)
#>        A        B 
#> 35.91079 35.25483

22.5.2.1 Rerandomization Criterion

Re-randomization is based on a function of the covariate matrix ($\mathbf{X}$) and treatment assignments ($\mathbf{W}$).

$$W_i = \begin{cases} 1, & \text{if treated} \\ 0, & \text{if control} \end{cases}$$

A common approach is to use Mahalanobis Distance to measure covariate balance between treatment and control groups:

$$\begin{aligned} M &= (\bar{\mathbf{X}}_T - \bar{\mathbf{X}}_C)' \text{cov}(\bar{\mathbf{X}}_T - \bar{\mathbf{X}}_C)^{-1} (\bar{\mathbf{X}}_T - \bar{\mathbf{X}}_C) \\ &= (\frac{1}{n_T} + \frac{1}{n_C})^{-1} (\bar{\mathbf{X}}_T - \bar{\mathbf{X}}_C)' \text{cov}(\mathbf{X})^{-1} (\bar{\mathbf{X}}_T - \bar{\mathbf{X}}_C) \end{aligned}$$

where:

• $\bar{\mathbf{X}}_T$ and $\bar{\mathbf{X}}_C$ are the mean covariate values for treatment and control groups.
• $\text{cov}(\mathbf{X})$ is the covariance matrix of the covariates.
• $n_T$ and $n_C$ are the sample sizes for treatment and control groups.

If the sample size is large and randomization is pure, then $M$ follows a chi-squared distribution:

$$M \sim \chi^2_k$$

where $k$ is the number of covariates to be balanced.

---

Choosing the Rerandomization Threshold ($M > a$)

Define $p_a$ as the probability of accepting a randomization:

• Smaller $p_a$ → Stronger balance, but longer computation time.
• Larger $p_a$ → Faster randomization, but weaker balance.

A rule of thumb is to re-randomize whenever:

$$M > a$$

where $a$ is chosen based on acceptable balance thresholds.

---

We apply Mahalanobis Distance as a balance criterion to ensure that treatment and control groups are well-matched before proceeding with the experiment.

set.seed(123)
library(MASS)

# Generate a dataset with two covariates
n <- 100
X <- mvrnorm(n, mu = c(0, 0), Sigma = matrix(c(1, 0.5, 0.5, 1), 2, 2))
colnames(X) <- c("Covariate1", "Covariate2")

# Balance function using Mahalanobis Distance
balance_criterion <- function(X, group) {
  X_treat <- X[group == 1, ]
  X_control <- X[group == 0, ]
  
  mean_diff <- colMeans(X_treat) - colMeans(X_control)
  cov_inv <- solve(cov(X))
  M <- t(mean_diff) %*% cov_inv %*% mean_diff
  
  
  # Acceptable threshold 
  # (chi-squared critical value for k = 2, alpha = 0.05)
  return(M < 3.84)  
}

# Repeat randomization until balance is met
repeat {
  group <- sample(c(0, 1), n, replace = TRUE)
  if (balance_criterion(X, group)) break
}

# Display final balance check
table(group)
#> group
#>  0  1 
#> 50 50
colMeans(X[group == 1, ])  # Treatment group means
#> Covariate1 Covariate2 
#>  0.2469635  0.1918521
colMeans(X[group == 0, ])  # Control group means
#>  Covariate1  Covariate2 
#>  0.01717088 -0.14281124

22.5.3 Two-Stage Randomized Experiments

A two-stage randomized experiment involves sequential interventions, where treatment assignments depend on earlier responses. This design is widely used in:

• Adaptive Learning: Adjusting educational content based on student progress.
• Personalized Advertising: Targeting follow-up ads based on engagement.
• Medical Trials: Adapting treatments based on patient response.

By introducing a second randomization stage, researchers can evaluate:

1. The effect of initial treatments.
2. The effect of follow-up treatments.
3. Potential interactions between the two stages.

---

22.5.3.1 Example: Personalized Advertising Experiment

A company tests two initial ad campaigns (Ad A vs. Ad B). After observing customer engagement, they apply a second-stage intervention (e.g., Discount vs. No Discount).

The two-stage experiment can be modeled as:

$$Y_{ijk} = \mu + \alpha_i + \beta_{j(i)} + \epsilon_{ijk}$$

where:

• $\mu$ = Overall mean outcome (e.g., conversion rate).
• $\alpha_i$ = Effect of first-stage intervention ($i$ = Ad A or Ad B).
• $\beta_{j(i)}$ = Effect of second-stage intervention, nested within first-stage groups.
• $\epsilon_{ijk}$ = Random error term.

The nested structure ensures that the second-stage treatment ($\beta_{j(i)}$) is assigned within each first-stage treatment group.

---

set.seed(123)

# Generate first-stage randomization (Initial Ad)
data <- data.frame(
  stage1 = sample(c("Ad A", "Ad B"), 100, replace = TRUE),
  stage2 = rep(NA, 100)  # Placeholder for second-stage randomization
)

# Second-stage assignment based on first-stage response
data$stage2[data$stage1 == "Ad A"] <-
    sample(c("Discount", "No Discount"),
           sum(data$stage1 == "Ad A"),
           replace = TRUE)
data$stage2[data$stage1 == "Ad B"] <-
    sample(c("Discount", "No Discount"),
           sum(data$stage1 == "Ad B"),
           replace = TRUE)

# Display final randomization
table(data$stage1, data$stage2)
#>       
#>        Discount No Discount
#>   Ad A       24          33
#>   Ad B       22          21

This structure ensures:

1. Fair assignment of initial ads.

2. Adaptive targeting in the second stage based on user engagement.

22.5.4 Two-Stage Randomized Experiments with Interference and Noncompliance

In real-world experiments, interference and noncompliance complicate analysis (Imai, Jiang, and Malani 2021):

• Interference: When treatment effects “spill over” from one group to another (e.g., social influence in marketing).

• Noncompliance: When participants do not adhere to their assigned treatment (e.g., a customer ignoring an ad).

To handle noncompliance, we define:

• $Z_{ik}$ = Assigned treatment (e.g., Ad A or Ad B).

• $D_{ik}$ = Actual treatment received (e.g., whether the user actually saw the ad).

• $Y_{ik}$ = Outcome (e.g., purchase).

A two-stage Instrumental Variable model adjusts for noncompliance:

$$\begin{aligned} D_{ik} &= \gamma_0 + \gamma_1 Z_{ik} + v_{ik} \\ Y_{ik} &= \beta_0 + \beta_1 D_{ik} + \epsilon_{ik} \end{aligned}$$ where:

• $\gamma_1$ measures the effect of assignment on actual treatment received.

• $\beta_1$ estimates the treatment effect, adjusting for noncompliance.

If individuals influence each other’s outcomes, traditional randomization is biased. Solutions include:

1. Cluster Randomization: Assigning treatments at the group level (e.g., entire social circles receive the same ad).

2. Partial Interference Models: Assume interference only occurs within predefined groups.

set.seed(123)
library(ivreg)  # Load Instrumental Variable Regression Package

# Generate data for first-stage treatment assignment
n <- 500
data <- data.frame(
  Z = sample(c(0, 1), n, replace = TRUE),  # Initial assignment (randomized)
  D = NA,  # Actual treatment received (affected by compliance)
  Y = NA   # Outcome variable (e.g., purchase)
)

# Introduce noncompliance: 80% compliance rate
data$D <- ifelse(runif(n) < 0.8, data$Z, 1 - data$Z)

# Generate outcome variable (Y) with true treatment effect
# True effect of D on Y is 3
data$Y <- 5 + 3 * data$D + rnorm(n, mean = 0, sd = 2)  

# Estimate Two-Stage Least Squares (2SLS)
iv_model <- ivreg(Y ~ D | Z, data = data)
summary(iv_model)
#> 
#> Call:
#> ivreg(formula = Y ~ D | Z, data = data)
#> 
#> Residuals:
#>    Min     1Q Median     3Q    Max 
#> -5.497 -1.344  0.018  1.303  5.493 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)   5.1285     0.1861  27.557   <2e-16 ***
#> D             2.7487     0.3072   8.949   <2e-16 ***
#> 
#> Diagnostic tests:
#>                  df1 df2 statistic p-value    
#> Weak instruments   1 498    263.54  <2e-16 ***
#> Wu-Hausman         1 497      0.19   0.663    
#> Sargan             0  NA        NA      NA    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 2.017 on 498 degrees of freedom
#> Multiple R-Squared: 0.2997,  Adjusted R-squared: 0.2983 
#> Wald test: 80.08 on 1 and 498 DF,  p-value: < 2.2e-16

• The first-stage regression estimates how strongly $Z$ affects $D$ (compliance).

• The second-stage regression estimates the true causal effect of $D$ on $Y$.

If interference is present, the standard IV method may be biased. Researchers should explore network-based randomization or spatial models.

22.6 Emerging Research

Recent research highlights significant challenges in measuring the causal effects of ad content in online advertising experiments. Digital advertising platforms employ algorithmic targeting and dynamic ad delivery mechanisms, which can introduce systematic biases in A/B testing (Braun and Schwartz 2025). Key concerns include:

• Nonrandom Exposure: Online advertising platforms optimize ad delivery dynamically, meaning users are not randomly assigned to different ad variants. Instead, platforms use proprietary algorithms to serve ads to different, often highly heterogeneous, user groups.

• Divergent Delivery: The optimization process can lead to “divergent delivery,” where differences in user engagement patterns and platform objectives result in non-comparable treatment groups. This confounds the true effect of ad content with algorithmic biases in exposure.

• Bias in Measured Effects: Algorithmic targeting, user heterogeneity, and data aggregation can distort both the magnitude and direction of estimated ad effects. This means traditional A/B test results may not accurately reflect the true impact of ad creatives.

• Limited Transparency: Platforms have little incentive to assist advertisers or researchers in disentangling ad content effects from proprietary targeting mechanisms. As a result, experimenters must design careful identification strategies to mitigate these biases.

J. Zhao and Zhou (2024)

• Addresses the challenge of covariate balancing in online A/B testing when experimental subjects arrive sequentially.

• Introduces the online blocking problem, where subjects with heterogeneous covariates must be immediately assigned to control or treatment groups.

• Aims to minimize total discrepancy, measured as the minimum weight perfect matching between groups.

• Proposes a pigeonhole design, a randomized experimental design that:

  • Partitions the covariate space into smaller subspaces (“pigeonholes”).

  • Balances the number of treated and control subjects within each pigeonhole.

• Theoretical analysis demonstrates the effectiveness of the pigeonhole design.

• Compares against matched-pair design and completely randomized design, identifying scenarios where the pigeonhole design performs better.

• Empirical validation using Yahoo! data shows a 10.2% reduction in variance in estimating the average treatment effect.

• Highlights practical implications for improving covariate balance in real-world online experiments.

22.6.1 Covariate Balancing in Online A/B Testing: The Pigeonhole Design

J. Zhao and Zhou (2024) address the challenge of covariate balancing in online A/B testing when experimental subjects arrive sequentially. Traditional experimental designs struggle to maintain balance in real-time settings where treatment assignments must be made immediately. This work introduces the online blocking problem, in which subjects with heterogeneous covariates must be assigned to treatment or control groups dynamically, with the goal of minimizing total discrepancy—quantified as the minimum weight perfect matching between groups.

To improve covariate balance, the authors propose the pigeonhole design, a randomized experimental design that operates as follows:

• The covariate space is partitioned into smaller subspaces called “pigeonholes.”
• Within each pigeonhole, the design balances the number of treated and control subjects, reducing systematic imbalances.

Theoretical analysis demonstrates the effectiveness of this design in reducing discrepancy compared to existing approaches. Specifically, the pigeonhole design is benchmarked against:

• Matched-pair design
• Completely Randomized Design

The results highlight scenarios where the pigeonhole design outperforms these alternatives in maintaining covariate balance.

Using Yahoo! data, the authors validate the pigeonhole design in practice, demonstrating a 10.2% reduction in variance when estimating the Average Treatment Effect. This improvement underscores the design’s practical utility in reducing noise and improving the precision of experimental results.

The findings of J. Zhao and Zhou (2024) provide valuable insights for practitioners conducting online A/B tests:

• Ensuring real-time covariate balance improves the reliability of causal estimates.
• The pigeonhole design offers a structured yet flexible approach to balancing covariates dynamically.
• By reducing variance in treatment effect estimation, it enhances statistical efficiency in large-scale online experiments.

This study contributes to the broader discussion of randomized experimental design in dynamic settings, providing a compelling alternative to traditional approaches.

22.6.2 Handling Zero-Valued Outcomes

When analyzing treatment effects, a common issue arises when the outcome variable includes zero values.

For example, in business applications, a marketing intervention may have no effect on some customers (resulting in zero sales). If we were to apply a log transformation to the outcome variable, this would be problematic because:

• $\log(0)$ is undefined.
• Log transformation is sensitive to outcome units, making interpretation difficult (J. Chen and Roth 2023).

Instead of applying a log transformation, we should use methods that are robust to zero values:

1. Percentage changes in the Average
   • By using Poisson Quasi-Maximum Likelihood Estimation (QMLE), we can interpret coefficients as percentage changes in the mean outcome of the treatment group relative to the control group.
2. Extensive vs. Intensive Margins
   • This approach distinguishes between:
     • Extensive margin: The likelihood of moving from zero to a positive outcome (e.g., increasing the probability of making a sale).
     • Intensive margin: The increase in the outcome given that it is already positive (e.g., increasing the sales amount).
   • To estimate the intensive-margin bounds, we could use Lee (2009), assuming that treatment has a monotonic effect on the outcome.

---

We first generate a dataset to simulate a scenario where an outcome variable (e.g., sales, website clicks) has many zeros, and treatment affects both the extensive and intensive margins.

set.seed(123) # For reproducibility
library(tidyverse)

n <- 1000 # Number of observations
p_treatment <- 0.5 # Probability of being treated

# Step 1: Generate the treatment variable D
D <- rbinom(n, 1, p_treatment)

# Step 2: Generate potential outcomes
# Untreated potential outcome (mostly zeroes)
Y0 <- rnorm(n, mean = 0, sd = 1) * (runif(n) < 0.3)

# Treated potential outcome (affecting both extensive and intensive margins)
Y1 <- Y0 + rnorm(n, mean = 2, sd = 1) * (runif(n) < 0.7)

# Step 3: Combine effects based on treatment assignment
Y_observed <- (1 - D) * Y0 + D * Y1

# Ensure non-negative outcomes (modeling real-world situations)
Y_observed[Y_observed < 0] <- 0

# Create a dataset with an additional control variable
data <- data.frame(
    ID = 1:n,
    Treatment = D,
    Outcome = Y_observed,
    X = rnorm(n) # Control variable
) |>
    dplyr::mutate(positive = Outcome > 0) # Indicator for extensive margin

# View first few rows
head(data)
#>   ID Treatment   Outcome          X positive
#> 1  1         0 0.0000000  1.4783345    FALSE
#> 2  2         1 2.2369379 -1.4067867     TRUE
#> 3  3         0 0.0000000 -1.8839721    FALSE
#> 4  4         1 3.2192276 -0.2773662     TRUE
#> 5  5         1 0.6649693  0.4304278     TRUE
#> 6  6         0 0.0000000 -0.1287867    FALSE

# Plot distribution of outcomes
hist(
    data$Outcome,
    breaks = 30,
    main = "Distribution of Outcomes",
    col = "lightblue"
)

22.6.2.1 Estimating Percentage Changes in the Average

Since we cannot use a log transformation, we estimate percentage changes using Poisson QMLE, which is robust to zero-valued outcomes.

library(fixest)

# Poisson Quasi-Maximum Likelihood Estimation (QMLE)
res_pois <- fepois(
    fml = Outcome ~ Treatment + X, 
    data = data, 
    vcov = "hetero"
)

# Display results
etable(res_pois)
#>                           res_pois
#> Dependent Var.:            Outcome
#>                                   
#> Constant        -2.223*** (0.1440)
#> Treatment        2.579*** (0.1494)
#> X                  0.0235 (0.0406)
#> _______________ __________________
#> S.E. type       Heteroskedas.-rob.
#> Observations                 1,000
#> Squared Cor.               0.33857
#> Pseudo R2                  0.26145
#> BIC                        1,927.9
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

To interpret the results:

• The coefficient on Treatment represents the log-percentage change in the mean outcome of the treatment group relative to the control group.

• To compute the proportional effect, we exponentiate the coefficient:

# Compute the proportional effect of treatment
treatment_effect <- exp(coefficients(res_pois)["Treatment"]) - 1
treatment_effect
#> Treatment 
#>  12.17757

Compute the standard error:

# Compute standard error
se_treatment <- exp(coefficients(res_pois)["Treatment"]) *
    sqrt(res_pois$cov.scaled["Treatment", "Treatment"])
se_treatment
#> Treatment 
#>  1.968684

Thus, we conclude that the treatment effect increases the outcome by 1215% for the treated group compared to the control group.

22.6.2.2 Estimating Extensive vs. Intensive Margins

We now estimate treatment effects separately for the extensive margin (probability of a nonzero outcome) and the intensive margin (magnitude of effect among those with a nonzero outcome).

First, we use Lee Bounds to estimate the intensive-margin effect for individuals who always have a nonzero outcome.

library(causalverse)

res <- causalverse::lee_bounds(
    df = data,
    d = "Treatment",
    m = "positive",
    y = "Outcome",
    numdraws = 10
) |> 
    causalverse::nice_tab(2)

print(res)
#>          term estimate std.error
#> 1 Lower bound    -0.22      0.09
#> 2 Upper bound     2.77      0.14

Since the confidence interval includes zero, we cannot conclude that the treatment has a significant intensive-margin effect.

22.6.2.3 Sensitivity Analysis: Varying the Effect for Compliers

To further investigate the intensive-margin effect, we consider how sensitive our results are to different assumptions about compliers.

We assume that the expected outcome for compliers is $100 \times c%$ lower or higher than for always-takers: $$E(Y(1) \| \text{Complier}) = (1 - c) E(Y(1) \| \text{Always-taker})$$ We compute Lee Bounds for different values of $c$:

set.seed(1)
c_values = c(0.1, 0.5, 0.7)

combined_res <- bind_rows(lapply(c_values, function(c) {
    res <- causalverse::lee_bounds(
        df = data,
        d = "Treatment",
        m = "positive",
        y = "Outcome",
        numdraws = 10,
        c_at_ratio = c
    )
    
    res$c_value <- as.character(c)
    return(res)
}))

combined_res |> 
    dplyr::select(c_value, everything()) |> 
    causalverse::nice_tab()
#>   c_value           term estimate std.error
#> 1     0.1 Point estimate     6.60      0.71
#> 2     0.5 Point estimate     2.54      0.13
#> 3     0.7 Point estimate     1.82      0.08

• If we assume $c = 0.1$, meaning compliers’ outcomes are 10% of always-takers’, then the intensive-margin effect is 6.6 units higher for always-takers.
• If $c = 0.5$, meaning compliers’ outcomes are 50% of always-takers’, then the intensive-margin effect is 2.54 units higher.

These results highlight how assumptions about compliers affect conclusions about the intensive margin.

When dealing with zero-valued outcomes, log transformations are not appropriate. Instead:

• Poisson QMLE provides a robust way to estimate percentage changes in the outcome.

• Extensive vs. Intensive Margins allow us to distinguish between:

  • The probability of a nonzero outcome (extensive margin).

  • The magnitude of change among those with a nonzero outcome (intensive margin).

• Lee Bounds provide a method to estimate the intensive-margin effect, though results can be sensitive to assumptions about always-takers and compliers.