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 …

When assessing causal effects, determining the target population to which the results are
intended to generalize is a critical decision. Randomized and observational studies each …

Estimating individual treatment effects from observational data is challenging due to
treatment selection bias. Prevalent methods mainly mitigate this issue by aligning different …

Many scientific and engineering challenges—ranging from personalized medicine to
customized marketing recommendations—require an understanding of treatment effect …

Estimating individualized treatment effects (ITE) is a challenging task due to the need for an
individual's potential outcomes to be learned from biased data and without having access to …

In this chapter, we present econometric and statistical methods for analyzing randomized
experiments. For basic experiments, we stress randomization-based inference as opposed …

Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due
to the missing counterfactuals and the selection bias. Existing ITE estimation methods …

In this paper we propose methods for estimating heterogeneity in causal effects in
experimental and observational studies and for conducting hypothesis tests about the …

It is often claimed that only experiments can support strong causal inferences and therefore
they should be privileged in the behavioral sciences. We disagree. Overvaluing experiments …

There is a large theoretical literature on methods for estimating causal effects under
unconfoundedness, exogeneity, or selection-on-observables type assumptions using …