We develop an encompassing framework for matching, covariate balancing, and doubly-
robust methods for causal inference from observational data called generalized optimal …

We propose a novel framework for matching estimators for causal effect from observational
data that is based on minimizing the dual norm of estimation error when expressed as an …

We introduce a new “Monotonic Imbalance Bounding”(MIB) class of matching methods for
causal inference with a surprisingly large number of attractive statistical properties. MIB …

A classical problem in causal inference is that of matching, where treatment units need to be
matched to control units based on covariate information. In this work, we propose a method …

We address a major discrepancy in matching methods for causal inference in observational
data. Since these data are typically plentiful, the goal of matching is to reduce bias and only …

Matching methods for causal inference selectively prune observations from the data in order
to reduce model dependence. They are successful when simultaneously maximizing …

When estimating causal effects using observational data, it is desirable to replicate a
randomized experiment as closely as possible by obtaining treated and control groups with …

We compare score-based and constraint-based learning in the presence of latent
confounders. We use a greedy search strategy to identify the best fitting maximal ancestral …

We introduce a flexible framework that produces high-quality almost-exact matches for
causal inference. Most prior work in matching uses ad-hoc distance metrics, often leading to …

Applied research conditions often make it impossible to point-identify causal estimands
without untenable assumptions. Partial identification—bounds on the range of possible …