Causal-Inference

Origins of Randomization (and the Rise of Design) for Causal Inference

A Counterfactual Perspective of Heritability, Explainability, and ANOVA

Existing tools for explaining complex models and systems are associational rather than causal and do not provide mechanistic understanding. Motivated by the concept of genetic heritability in twin studies, this talk will introduce a new notion called …

Heritability: a counterfactual perspective

Heritability is a central concept in the long-standing debate about nature versus nurture in biological and social sciences. However, existing notions of heritability are based on strong modeling assumptions, and the models are statistical rather …

Proximal causal identification using a hidden tetrad constraint

We provide an alternative derivation of the proximal causal identification formula in Kuroki and Pearl [2014] using a 'hidden' tetrad constraint.

Acyclic Directed Mixed Graphs: Matrix Algebra, Statistical Models, Confounder Selection

Selective Randomization Inference for Adaptive Experiments

On statistical and causal models associated with acyclic directed mixed graphs

A talk that goes over [this paper](publication/admg-model/)

Counterfactual explainability and analysis of variance

We propose a counterfactual notion of explainability.

A graphical approach to state variable selection in off-policy learning

We give graphical criteria for state variables to be 'valid' in off-policy learning in a framework that generalizes dynamic treatment regimes (DTRs) and Markov decision processes (MDPs).

On statistical and causal models associated with acyclic directed mixed graphs

Causal models in statistics are often described by acyclic directed mixed graphs (ADMGs), which contain directed and bidirected edges and no directed cycles. This article surveys various interpretations of ADMGs, discusses their relations in …