Relaxes conditional independence assumption
Continuous, binary, count, fractional, and nonnegative outcomes
Average treatment effects (ATEs)
ATEs on the treated (ATETs)
Potential-outcome means (POMs)
Treatment-effects estimators extract experimental-style causal effects from observational data. Conventional treatment-effects estimators require the conditional independence assumption. That is, we must assume that no unobserved variables affect both treatment assignment and the outcome.
If an unobserved variable affects which treatment a person gets and affects the outcome, we have an endogeneity problem and we cannot obtain accurate estimates of effects using conventional treatment-effects estimators. Endogenous treatment estimators address such cases.
Stata has three commands for endogenous treatment-effects estimation.
We can estimate endogenous treatment effects in the same potential-outcomes framework used by teffects—the parameters of interest are the treatment effects. It lets us model a wide range of outcomes: continuous, binary, count, fractional, and nonnegative outcomes.
We can also estimate a linear or Poisson regression model that includes an endogenous treatment by using either etregress or etpoisson. These commands are slightly different from eteffects. Because the methods implemented in these commands are not naturally in the potential-outcomes framework, we use margins to obtain treatment effects such as the ATE.
Read much more about endogenous treatment effects and see more examples in [CAUSAL] eteffects, [CAUSAL] etregress, and [CAUSAL] etpoisson, which can be found in Stata Causal Inference and Treatment-Effects Estimation Reference Manual.
Learn about extended regression models, which can account for endogenous treatment effects along with endogenous covariates and sample selection.