Annotated Bibliography: Modern Causal Inference and Targeted Learning

This bibliography introduces key readings in modern causal inference, targeted learning, and real-world evidence generation. Each annotation summarizes why the paper is important and what it contributes to causal reasoning and applied biostatistics.

to add:

Targeted learning in real-world comparative effectiveness research with time-varying interventions https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.6099

1. Causal Inference Roadmap and Target Trial Emulation

Dang et al. (2023) – A causal roadmap for generating high-quality real-world evidence.
Introduces the Causal Roadmap framework for structuring causal analyses in observational data. Emphasizes transparency, assumptions, and reproducibility in real-world evidence.

Gruber et al. (2023) – Evaluating and improving real-world evidence with Targeted Learning.
Applies the roadmap to re-analyze published results using TMLE, highlighting the link between causal identification and robust estimation.

Williamson et al. (2023) – An application of the Causal Roadmap in two safety monitoring case studies.
Demonstrates roadmap principles in practice for safety monitoring and outcome prediction using electronic health records.

Hernán & Robins (2016) – Using big data to emulate a target trial when a randomized trial is not available.
Defines target trial emulation, a cornerstone idea for translating causal inference principles to observational study design.

2. Estimand Specification in Clinical Studies

ICH E9 (R1) Addendum (2019) – Addendum on Estimands and Sensitivity Analyses in Clinical Trials.
Establishes the estimand framework to align clinical trial objectives, analyses, and interpretations.

Rufibach (2019) – Treatment effect quantification for time-to-event endpoints – Estimands, analysis strategies, and beyond.
Applies the estimand framework to survival outcomes, clarifying how censoring and non-proportional hazards affect effect interpretation.

3. Super Learner Ensemble Learning

van der Laan, Polley & Hubbard (2007) – Super Learner.
A foundational paper introducing ensemble learning via cross-validation for optimal prediction and causal estimation.

Phillips et al. (2023) – Practical considerations for specifying a Super Learner.
A practical tutorial on constructing and validating Super Learners, including library specification, cross-validation, and reproducibility.

4. Targeted Maximum Likelihood Estimation (TMLE)

Gruber & van der Laan (2010) – Targeted Maximum Likelihood Estimation: A Gentle Introduction.
Provides a clear step-by-step introduction to TMLE, integrating machine learning and influence function theory for efficient, doubly robust estimation.

5. Time-Dependent Confounding and Intercurrent Events

Petersen (2014) – Applying a Causal Road Map in Settings with Time-dependent Confounding.
Discusses longitudinal causal inference and how to handle time-dependent confounding through g-methods and TMLE.

Stensrud et al. (2019) – Limitations of hazard ratios in clinical trials.
Explains why hazard ratios can be misleading as causal measures and encourages absolute or survival-based contrasts.

Martinussen (2022) – Causality and the Cox Regression Model.
Clarifies the mathematical and conceptual limitations of hazard ratios, advocating more interpretable causal estimands.

6. Dynamic Treatment Regimes and Stochastic Interventions

Chakraborty & Murphy (2014) – Dynamic Treatment Regimes.
A comprehensive overview of adaptive treatment strategies and their estimation through sequential designs and reinforcement learning methods.

Kennedy (2019) – Nonparametric causal effects based on incremental propensity score interventions.
Introduces stochastic interventions that shift treatment probabilities incrementally, addressing positivity violations and improving policy relevance.