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Learn about causal inference and causal-inference analysis.

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

See Heterogeneous difference in differences (DID).

See Treatment-effects lasso estimation.

Estimators

• Inverse-probability weights (IPW)
• Propensity-score matching
• Covariate matching
• Regression adjustment
• Weighted regression
• Doubly robust methods
  • Augmented IPW (AIPW) StataNow
  • IPW with regression adjustment
  • AIPW using lasso
• Difference in differences (DID)
• Heterogeneous DID for cross-sectional data New
• Heterogeneous DID for panel-data New
• Difference-in-difference-in-differences (DDD)
• Panel data

Endogeneity, Heckman-style selection, and panel data with causal 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

• Overlap plots
• Covariate balance

Postestimation Selector

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

Causal mediation analysis New

• Continuous, binary, and count outcomes
• Continuous, binary, and count mediators
• Binary, multivalued, and continuous treatments
• Linear, logit, probit, Poisson, and exponential mean models
• Direct effects, indirect effects, total effects, and POMs