Treatment effects/Causal inference
Stata's treatment effects allow you to estimate experimental-type causal effects from observational data. Whether you are interested in a continuous, binary, count, fractional, or survival outcome; whether you are modeling the outcome process or treatment process; Stata can estimate your treatment effect. With the most comprehensive set of treatment-effects estimators available in any software package, you will find the one that's right for you.
- Inverse-probability weights (IPW)
- Propensity-score matching
- Covariate matching
- Regression adjustment
- Weighted regression
- Doubly robust methods
- Augmented IPW (AIPW)
- IPW with regression adjustment
- Augmented IPW (AIPW)
Watch Inverse-probability weighting.
Watch Inverse-probability weights (IPW) with RA.
Watch Augmented IPW.
Watch Nearest-neighbor matching.
Watch Propensity-score matching.
Endogeneity, Heckman-style selection, and panel data with treatment effects
- Linear regression
- Interval regression, including tobit
- Probit regression
- Ordered probit regression
- Exogenous or endogenous regressors
- Endogenous or exogenous treatment; binary or ordinal treatment
- Random-effects models for panel data New
Statistics
- Average treatment effects (ATEs)
- ATEs on the treated (ATETs)
- Potential-outcome means (POMs)
Outcomes
- Continuous—linear
- Binary—logistic, probit, heteroskedastic probit
- Count—Poisson
- Fractional
- Nonnegative, including exponential mean
- Survival—exponential, Weibull, gamma, lognormal
Treatments
- Binary—logistic, probit, heteroskedastic probit
- Multivalued-multinomial logistic
Diagnostics
- Overlap plots
- Covariate balance
- View and run all postestimation features for your command
- Automatically updated as estimation commands are run
- Continuous outcome
- Count outcome
- Control-function estimator
- ATEs, ATETs, and POMs
- Test for endogeneity
Additional resources
- Treatment-Effects Reference Manual
- Extended Regression Models Reference Manual
- In the spotlight: Double-robust treatment effects (two wrongs don't make a right, but one does)
- In the spotlight: Treatment effects
- In the spotlight: eteffects and the challenges of making causal inferences
- The Stata Blog: Introduction to treatment effects in Stata: Part 1
- The Stata Blog: Introduction to treatment effects in Stata: Part 2
- Causal Inference Using Stata: Estimating Average Treatment Effects training course
Watch A tour of treatment effects.
Watch Introduction to treatment effects, part 1.
Watch Introduction to treatment effects, part 2.
See New in Stata 16 for more about what was added in Stata 16.
