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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

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


  • Average treatment effects (ATEs)
  • ATEs on the treated (ATETs)
  • Potential-outcome means (POMs)


  • Continuous—linear
  • Binary—logistic, probit, heteroskedastic probit
  • Count—Poisson
  • Fractional
  • Nonnegative, including exponential mean
  • Survival—exponential, Weibull, gamma, lognormal


  • Binary—logistic, probit, heteroskedastic probit
  • Multivalued-multinomial logistic


Postestimation Selector

  • View and run all postestimation features for your command
  • Automatically updated as estimation commands are run

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.





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