# Chapter 3 Correlation & Causation

## 3.1 Introduction

## 3.2 A First Definition of Causality

We quantify causality by using the notion of the causal relation introduced by Granger (Wiener 1956; Granger 1969) where a signal \(X\) is said to Granger-cause \(Y\) if the future realizations of \(Y\) can be better explained using the past information from \(X\) and \(Y\) rather than \(Y\) alone.

The most common definitions of Granger-causality rely on the prediction of a future value of the variable \(Y\) by using the past values of \(X\) and \(Y\) itself. In that form, \(X\) is said to *G-cause* \(Y\) if the use of \(X\) improves the prediction of \(Y\).

Let \(X_t\) be a random variable associated at time \(t\) while \(X^t\) represents the collection of random variables up to time \(t\). We consider \({X_t}, {Y_t}\) and \({Z_t}\) to be three stochastic processes. Let \(\hat Y_{t+1}\) be a predictor for the value of the variable \(Y\) at time \(t+1\).

We compare the expected value of a loss function \(g(e)\) with the error \(e=\hat{Y}_{t+1} - Y_{t+1}\) of two models:

- The expected value of the prediction error given only \(Y^t\) \[\begin{equation} \mathcal{R}(Y^{t+1} \, | \, Y^t,Z^t) = \mathbb{E}[g(Y_{t+1} - f_1(X^{t},Z^t))] \end{equation}\]
- The expected value of the prediction error given \(Y^t\) and \(X^t\) \[\begin{equation} \mathcal{R}(Y^{t+1} \, | \, X^{t},Y^t,Z^t) = \mathbb{E}[g(Y_{t+1} - f_2(X^{t},Y^t,Z^t))]. \end{equation}\]

In both models, the functions \(f_1(.)\) and \(f_2(.)\) are chosen to minimize the expected value of the loss function. In most cases, these functions are retrieved with linear and, possibly, with nonlinear regressions. Typical forms for \(g(.)\) are the \(l1\)- or \(l2\)-norms.

We can now provide our first definition of statistical causality under the Granger causal notion as follows:

**Definition 3.1**\(X\) does not Granger-cause \(Y\) relative to side information \(Z\) if and only if \(\mathcal{R}(Y_{t+1} \; | \; X^t, Y^t, Z^t) = \mathcal{R}(Y_{t+1} \; | \; Y^t, Z^t)\).

A more general definition than Def. 3.1 that does not depend on assuming prediction functions can be formulated by considering conditional probabilities. A probabilistic definition of G-causality assumes that \(Y_{t+1}\) and \(X^{t}\) are independent given the past information \((X^{t}, Y^{t})\) if and only if \(p(Y_{t+1} \, | \, X^{t}, Y^{t}, Z^{t}) = p(Y_{t+1} \, | \, Y^{t}, Z^{t})\), where \(p(. \, | \, .)\) represents the conditional probability distribution. In other words, omitting past information from \(X\) does not change the probability distribution of \(Y\). This leads to our second definition of statistical causality as follows:

**Definition 3.2**\(X\) does not Granger-cause \(Y\) relative to side information \(Z\) if and only if \(Y_{t+1} {\perp\!\!\!\!\perp}X^{t} \; | \; Y^{t}, Z^{t}\).

Def. 3.2 does not assume any functional form in the coupling between \(X\) and \(Y\). Nevertheless, it requires a method to assess their conditional dependency.

In the next Section, we define a parametric linear specification of G-causality based on Def. 3.1. Later in the book, in the Section 6.2, when we cover Econophysics techniques, we will present a nonlinear specification for G-causality based on Def. 3.2.

## 3.3 Quantifying Granger-Causality

We will take the following procedure to quantify Granger-causality according to Def. 3.1:

- Specify two predictive models:

- The first considers \(Y^t\) to predict \(Y^{t+1}\) (Model \(\mathcal{M}\));
- The second considers \(Y^t\) and \(X^t\) to predict \(Y^{t+1}\) (Model \(\mathcal{M}^*\));

- Test for model misspecification;
- Test the hypothesis that the expected value of the prediction error of the Models \(\mathcal{M}\) and \(\mathcal{M}^*\) are statistically the same;
- Apply correction for multiple hypothesis testing.

If the null hypothesis from 3. is rejected then there is evidence that \(X\) Granger-causes \(Y\) under Def. 3.1.

### 3.3.1 Model Specification

Standard Granger-causality tests assume a linear relationship among the causes and effects and are implemented by fitting autoregressive models (Wiener 1956; Granger 1969).

Consider the linear vector-autoregressive (VAR) equations: \[\begin{align} Y(t) &= {\alpha} + \sum^k_{\Delta t=1}{{\beta}_{\Delta t} Y(t-\Delta t)} + \epsilon_t, \tag{3.1}\\ Y(t) &= \widehat{\alpha} + \sum^k_{\Delta t=1}{{\widehat{\beta}}_{\Delta t} Y(t-\Delta t)} + \sum^k_{\Delta t=1}{{\widehat{\gamma}}_{\Delta t}X(t-\Delta t)}+ \widehat{\epsilon}_t, \tag{3.2} \end{align}\]where \(k\) is the number of lags considered.

From Def 3.1, \(X\) does not G-cause \(Y\) if and only if the prediction errors of \(X\) in the restricted Eq. (3.1) and unrestricted regression models Eq. (3.2) are equal (i.e., they are statistically indistinguishable).

### 3.3.2 Test for Misspecification

A statistically significant causality can be reported only if the linear models from Eqs. (3.1) and (3.2) are not misspecified. For that purpose, we utilize the BDS test (Brock et al. 1996) for the model misspecification.

The BDS test (Brock et al. 1996) is used to detect nonlinear dependence in time series. When applied to the residuals of a linear model, the BDS tests the null hypothesis that these residuals are independent and identically distributed. The BDS test is a powerful test to detect linear misspecification and nonlinearity (Brock et al. 1996; W. A. Barnett et al. 1997).

Let \(\epsilon_t = (\epsilon_{t=1}, \ldots, \epsilon_{t=n})\) be the residuals of the linear fitted model and define its \(m\)-embedding as \(\epsilon_t^m = (\epsilon_{t}, \epsilon_{t-1}, \ldots, \epsilon_{t-m+1})\). The \(m\)-embedding correlation integral is given by \[\begin{align} C_{m,n}(\Delta \epsilon) = \frac{2}{k(k-1)}\sum_{s = 1}^{t}{\sum_{t=s}^{n}{ \chi(\| \epsilon_s^m - \epsilon_t^m \|, \Delta \epsilon) }}, \nonumber \end{align}\] and \[\begin{align} C_{m}(\Delta \epsilon) = \lim_{n\to\infty} C_{m,n}(\Delta \epsilon), \nonumber \end{align}\]where \(\chi\) is an indicator function where \(\chi(\| \epsilon_s^m - \epsilon_t^m \|, \Delta \epsilon) = 1\) if \(\| \epsilon_s^m - \epsilon_t^m \| < \Delta \epsilon\) and zero, otherwise.

The null hypothesis of the BDS test assumes that \(\epsilon_t\) is iid. In this case, \[\begin{align} C_{m}(\Delta \epsilon) = C_{1}(\Delta \epsilon)^m. \nonumber \end{align}\] The BDS statistic is a measure of the extent that this relation holds in the data. This statistic is given by the following: \[\begin{align} V_{m}(\Delta \epsilon) = \sqrt{n}\frac{C_{m}(\Delta \epsilon) - C_{1}(\Delta \epsilon)^m}{\sigma_m(\Delta \epsilon)}, \nonumber \end{align}\]where \(\sigma_m(\Delta \epsilon)\) can be estimated as described in (Brock et al. 1996).

The null hypothesis of the BDS test indicates that the model tested is not misspecified and it is rejected at the 5% significance level if \(\|V_m(\Delta \epsilon)\| > 1.96\). The parameter \(\Delta \epsilon\) is commonly set as a factor of the variance (\(\sigma_\epsilon\)) of \(\epsilon\).

### 3.3.3 Analysis of Variance

A one-way ANOVA test is utilized to test if the residuals from Eqs. (3.1) and (3.2) differ from each other significantly.

### 3.3.4 Multiple Hypotheses Testing Correction

When more than one lag \(k\) is tested, a Bonferroni correction is applied to control for multiple hypotheses testing.

## 3.4 Conclusion

### References

Wiener, N. 1956. “The theory of prediction.” In *Modern Mathematics for Engineers*, edited by E. F. Beckenbach. McGraw-Hill, New York.

Granger, Clive. 1969. “Investigating Causal Relations by Econometric Models and Cross-Spectral Methods.” *Econometrica* 37 (3): 424–38.

Brock, W. A., J. A. Scheinkman, W. D. Dechert, and B. LeBaron. 1996. “A test for independence based on the correlation dimension.” *Econometric Reviews* 15 (3). Taylor & Francis: 197–235. doi:10.1080/07474939608800353.

Barnett, William A., A. Ronald Gallant, Melvin J. Hinich, Jochen A. Jungeilges, Daniel T. Kaplan, and Mark J. Jensen. 1997. “A Single-Blind Controlled Competition Among Tests for Nonlinearity and Chaos.” *Journal of Econometrics* 82: 157–92.