In causal inference, the joint law of a set of counterfactual random variables is generally not
identified. We show that a conservative version of the joint law-corresponding to the smallest …

The causal dose response curve is commonly selected as the statistical parameter of
interest in studies where the goal is to understand the effect of a continuous exposure on an …

Existing statistical methods in causal inference often rely on the assumption that every
individual has some chance of receiving any treatment level regardless of its associated …

Large observational studies have become commonplace in medical research. Treatment
may be adapted to covariates at several instances without a fixed protocol. Estimation or …

When extending inferences from a randomized trial to a new target population, an
assumption of transportability of difference effect measures (eg, conditional average …

We propose a nonlinear difference-in-differences (DiD) method to estimate multivariate
counterfactual distributions in classical treatment and control study designs with …

Many problems ask a question that can be formulated as a causal question: what would
have happened if...? For example, would the person have had surgery if he or she had been …

We focus on estimating causal effects of continuous treatments (eg, dosage in medicine),
also known as dose-response function. Existing methods in causal inference for continuous …

Imbalance in covariate distributions leads to biased estimates of causal effects. Weighting
methods attempt to correct this imbalance but rely on specifying models for the treatment …

We consider the problem of causal inference based on observational data (or the related
missing data problem) with a binary or discrete treatment variable. In that context we study …