Causal-Inference

On statistical 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 …

Post-selection inference for effect modification

A matrix algebra for graphical statistical models

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 …

Selective Randomization Inference for Adaptive Experiments

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

Design: The Elusive Principle of Statistics

Sensitivity analysis for observational studies: past, present, and future

Causal mediation analysis for time-varying heritable risk factors with Mendelian Randomization

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

Confounder selection via iterative graph expansion

Sensitivity analysis via stochastic programming

Confounder selection via iterative graph expansion