ESTIMATORS

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

Video – Treatment-effects estimation using lasso
Video – Regression adjustment (RA)
Video – Inverse-probability weighting
Video – Inverse-probability weights (IPW) with RA
Video – Augmented IPW
Video – Nearest-neighbor matching
Video – Propensity-score matching
Video – Heterogeneous difference in differences

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

• 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

• Continuous outcome
• Count outcome
• Control-function estimator
• ATEs, ATETs, and POMs
• Test for endogeneity

DIFFERENCE-IN-DIFFERENCES (DID) AND TRIPLE-DIFFERENCES (DDD) ESTIMATION

• 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

• 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