Causal-Inference

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

We propose a counterfactual notion of explanability for black-box prediction models.

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 …

We apply ideas from post-selection inference to randomization tests for adaptive experiments.

A new Bayesian full-information method for life-course Mendelian randomization