In settings that exhibit selection on both levels and gains, marginal treatment effects (MTE)
allow us to go beyond local average treatment effects and estimate the whole distribution of …

We describe the new command margte, which computes marginal and average treatment
effects for a model with a binary treatment and a continuous outcome given selection on …

We offer a propensity score perspective to interpret and analyze the marginal treatment
effect (MTE). Specifically, we redefine MTE as the expected treatment effect conditional on …

Estimation of average treatment effects under unconfoundedness or exogenous treatment
assignment is often hampered by lack of overlap in the covariate distributions. This lack of …

Because the average treatment effect (ATE) measures the change in social welfare, even if
positive, there is a risk of negative effect on, say, some 10% of the population. Assessing …

Treatment effects may vary with the observed characteristics of the treated, often with
important implications. In the context of experimental data, a growing literature deals with the …

There is a large literature on semiparametric estimation of average treatment effects under
unconfounded treatment assignment in settings with a fixed number of covariates. More …

Instrumental variables (IV) regression is widely used to estimate causal treatment effects in
settings where receipt of treatment is not fully random, but there exists an instrument that …

Understanding and characterizing treatment effect variation in randomized experiments has
become essential for going beyond the “black box” of the average treatment effect …

Program evaluations often focus on average treatment effects. However, average treatment
effects miss important aspects of policy evaluation, such as the impact on inequality and …