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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 
- Inverse-probability weights (IPW)

- Propensity-score matching

- Covariate matching

- Regression adjustment

- Weighted regression

- Doubly robust methods
- Difference in differences (DID)
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
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.