Causal Inference

Causal Inference. Potential outcomes, DAGs, and identification of causal effects. The literature on causal inference divides naturally along several axes: the foundational structures that organise the subject, the techniques that drive proofs and computations, the questions about classification or representation that animate current research, and the bridges to neighbouring areas of mathematics and science. The references below trace those axes through the canonical textbook treatments and recent technical contributions.

Foundations and canonical references

The standard treatments of causal inference approach the subject from complementary angles. Pearl, Causality: Models, Reasoning, and Inference (2009) is the anchor reference for the subject and lays out the core definitions, theorems, and worked examples that practitioners return to. Hernan, Causal Inference: What If (2020) gives a parallel, more proof-oriented exposition of the same material and is widely used as a graduate text. Peters, Elements of Causal Inference (2017) serves as a third pillar of the literature and is particularly strong on the structural and worked-out examples that the other references treat more briefly.

Open methodological questions for causal inference include sharpening the bridges between foundational theory and computational practice, extending classical results to broader or more structured settings, and integrating the techniques surveyed above with adjacent mathematical disciplines. The references listed in this page are the entry points that current work builds on.