The target of many empirical studies in the social, behavioral, and health sciences is the
causal effect, here denoted P (y| do (x)), which measures the total effect of a manipulated …

Previous researchers have argued that necessary and/or sufficient causes should be tested
through research designs that consider only cases with limited combinations of scores on …

We propose a simplified approach to matching for causal inference that simultaneously
optimizes balance (similarity between the treated and control groups) and matched sample …

Researchers seeking to establish causal relationships frequently control for variables on the
purported causal pathway, checking whether the original treatment effect then disappears …

Knowledge of causal effects is of great importance to decision makers in a wide variety of
settings. In many cases, however, these causal effects are not known to the decision makers …

A common motivation for adding control variables to statistical models is to reduce the
potential for spurious findings when analyzing non-experimental data and to thereby allow …

This paper introduces a simple framework of counterfactual estimation for causal inference
with time‐series cross‐sectional data, in which we estimate the average treatment effect on …

Many double-blind placebo-controlled randomized experiments with active drugs suffer from
complications beyond simple noncompliance. First, the compliance with assigned dose is …

Causal mediation analysis is widely used across many disciplines to investigate possible
causal mechanisms. Such an analysis allows researchers to explore various causal …

Difference-in-differences is a widely used evaluation strategy that draws causal inference
from observational panel data. Its causal identification relies on the assumption of parallel …