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Conditional average treatment effects (CATE) 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

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 HC2 and HC3 StataNow 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

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

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

Diagnostics

• Overlap plots
• Covariate balance

Postestimation Selector

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

Additional resources

• Causal Inference and Treatment-Effects Estimation Reference Manual
• Extended Regression Models Reference Manual
• Microeconometrics Using Stata, Second Edition, Volumes I and II by A. Colin Cameron and Pravin K. Trivedi
• 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
• Causal inference/Treatment effects flyer