Causal Inference Is Not Just a Statistics Problem

2023/12/01 by Lucy D’Agostino McGowan, Travis Gerke, Malcolm Barrett · 2 voices · 15 citations
Computer Science · Mathematics · #Advanced Causal Inference Techniques #Artificial intelligence #Bayesian Modeling and Causal Inference #Bayesian inference #Bayesian probability #Causal inference #Computer science #Econometrics #Fiducial inference #Frequentist inference #Inference #Mathematics #Statistical Methods and Inference #Statistics

paper · pdf · doi:10.1080/26939169.2023.2276446

published in Journal of Statistics and Data Science Education 32(2), 150-155 (Taylor & Francis)

openalex publication_date 2023/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/25

Abstract

This paper introduces a collection of four data sets, similar to Anscombe’s Quartet, that aim to highlight the challenges involved when estimating causal effects. Each of the four data sets is generated based on a distinct causal mechanism: the first involves a collider, the second involves a confounder, the third involves a mediator, and the fourth involves the induction of M-Bias by an included factor. The paper includes a mathematical summary of each data set, as well as directed acyclic graphs that depict the relationships between the variables. Despite the fact that the statistical summaries and visualizations for each data set are identical, the true causal effect differs, and estimating it correctly requires knowledge of the data-generating mechanism. These example data sets can help practitioners gain a better understanding of the assumptions underlying causal inference methods and emphasize the importance of gathering more information beyond what can be obtained from statistical tools alone. The paper also includes R code for reproducing all figures and provides access to the data sets themselves through an R package named quartets.

Citations

• Estimating causal effects of treatments in randomized and nonrandomized studies.
• Matching Using Estimated Propensity Scores: Relating Theory to Practice

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• Multifaceted Intervention to Improve Graft Outcome Disparities in African American Kidney Transplants (MITIGAAT Study): Protocol for a Randomized Controlled Trial. [europepmc]
• Spillover of macro-level violence to parental physical abuse of children in low- and middle-income countries. [europepmc]
• Toward New Directions in Human Biology: A Roadmap for Anthropological Causal Inference With Observational Data. [europepmc]

Discussions

• next up is a causal quartet showing that different causal mechanisms can lead to the same data D’Agostino McGowan, Lucy, Travis Gerke, and Malcolm Barrett. “Causal Inference Is Not Just a Statistics [bsky, 13 points, 3 comments]
• I am not sure about how can show that observational regression is often not causal (except that it seems plausible once knows more about causal modeling). Given that, the causal quartet may help: doi. [bsky, 2 points, 1 comments]

Related

• Causal inference is not just a statistics problem
• Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data
• Causal Inference: A Tale of Three Frameworks
• A Survey of Causal Inference Frameworks
• A Survey on Causal Discovery: Theory and Practice