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Causal inference/Treatment effects

Stata's causal-inference suite allows 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 causal-inference estimators available in any software package, you will find the one that's right for you.

Learn about causal inference and causal-inference analysis.

See what's new in causal inference.

Estimators

Conditional average treatment effects with honest random forest New

  • Treatment-effects heterogeneity at different levels
    • Individualized average treatment effect (IATE)
    • Group average treatment effect (GATE)
    • Sorted-group average treatment effect (GATES)
  • Flexible model specification (lasso, random forest, or parametric regression)
  • Evaluate treatment assignment policy
  • Treatment-effects visualization
    • Histogram of predicted IATEs
    • Plot the estimates of GATE or GATES
    • Plot of the IATE function
  • Toolbox of inferences on the treatment-effects heterogeneity
    • Predictions of the IATE function with confidence intervals
    • Tests whether the treatment effects are heterogeneous
    • Tests whether the estimated GATE or GATEs are statistically equal across group
    • Classification analysis of groups sorted by IATE
    • Linear approximation of the IATE function
    • Nonparametric series approximation of the IATE function

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

Postestimation Selector

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

Causal mediation analysis

  • 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

Endogenous treatment effects

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

  • DID and DDD estimators for repeated cross-sections data Updated
  • DID and DDD estimators for panel data Updated
  • 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
    • Bacon decomposition
    • Wild bootstrap
    • Donald–Lang estimator
    • Bias-corrected cluster–robust SEs
    • Bell–McCaffrey degrees of freedom

Heterogeneous DID

  • Four estimators
    • regression adjustment (RA)
    • inverse probability weighting (IPW)
    • augmented inverse probability weighting (AIPW)
    • two-way fixed-effects regression (TWFE)
  • Estimation of heterogeneous treatment effects
    • Panel data
    • Repeated cross-sectional data
  • Graphical representation of treatment effects
  • Estimate and visualize aggregations of ATETs within
    • cohort
    • time
    • exposure to treatment
  • Simultaneous confidence intervals

Treatment effects with high-dimensional controls

  • 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 Tour of treatment-effects estimators in Stata.

Watch Tour of treatment-effects estimators in Stata.
Watch Introduction to treatment effects, part 1.
Watch Introduction to treatment effects, part 2.

See New in Stata 19 to learn about what was added in Stata 19.