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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.

Learn about treatment-effects analysis.

See Difference-in-differences (DID) and DDD models.

See Treatment-effects lasso estimation.

Estimators

  • Inverse-probability weights (IPW)
  • Propensity-score matching
  • Covariate matching
  • Regression adjustment
  • Weighted regression
  • Doubly robust methods
  • Difference in differences (DID) New
    • Difference-in-difference-in-differences (DDD)
    • Panel data

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

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

Postestimation Selector

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

Endogenous treatment effects

Difference-in-differences (DID) and triple-differences (DDD)
estimation
New

  • DID and DDD estimators for repeated cross-sections data
  • DID and DDD estimators for panel data
  • DID diagnostics and tests
    • Test and graphs for parallel trends
    • Granger causality test
    • Time-specific treatment effects
  • ATET inference with small number of treatment and
    control groups
    • Wild bootstrap
    • Donald–Lang estimator
    • Bias-corrected cluster–robust SEs
    • Bell–McCaffrey degrees of freedom

Treatment effects with high-dimensional controls New

  • Continuous, binary, and count outcomes
  • Logit or probit treatment model
  • ATEs, ATETs, and POMs
  • Lasso or square-root lasso variable selection
  • Neyman orthogonal and doubly robust estimator
  • Double machine learning
  • Flexible model specification

Additional resources

dialog box for teffects

Watch A tour of treatment effects.
Watch Introduction to treatment effects, part 1.
Watch Introduction to treatment effects, part 2.

See New in Stata 17 to learn about what was added in Stata 17.

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