Causal-Inference

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 …

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

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

We propose a counterfactual notion of explainability.

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).

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 …

Directed mixed graphs permit directed and bidirected edges between any two vertices. They were first considered in the path analysis developed by Sewall Wright and play an essential role in statistical modeling. We introduce a matrix algebra for …