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PAA 2024 Annual Meeting
Columbus, OH | 17–20 April 2024

StataCorp will be an exhibitor at the PAA 2024 Annual Meeting. Attending from StataCorp: Enrique Pinzón, Director, Econometrics. We're also hosting a workshop. For more information about the meeting, visit the PAA 2024 Annual Meeting page.

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Stata workshop

Title: Causal inference and treatment effects using Stata
Presenter: Enrique Pinzon, Director, Econometrics
Date: Wednesday, 17 April 2024
Time: 1:00–5:00 p.m. ET
Description: In this workshop, we discuss methods for drawing causal inferences when analyzing observational rather than experimental data. We present a variety of estimators for average treatment effects (ATEs) and average treatment effects on the treated (ATETs) and discuss when each estimator is useful. Throughout the workshop, we cover the conceptual and theoretical underpinnings of treatment effects and demonstrate the methods with many practical examples worked using Stata software.
Materials: Download here

Enrique Pinzon

Enrique Pinzon portrait

Enrique Pinzón is the Director, Econometrics and part of the statistical development team at StataCorp LLC. He teaches a variety of Stata courses and is a frequent contributor to The Stata Blog. He holds a master's degree in economics from the Universidad de los Andes and a PhD from the University of Wisconsin–Madison.

PDF flyers

Browse the Stata flyers below.

Introduction to Stata



Stata overview
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Why Stata
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Stata 18 highlights
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Stata/MP
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Features



Bayesian analysis
Letter   A4

Bayesian model averaging
Letter   A4

Causal inference/Treatment effects
Letter   A4

Causal mediation analysis
Letter   A4

Choice models
Letter   A4

Clinical trials
Letter   A4

Customizable tables
Letter   A4

Data frames
Letter   A4

Difference in differences
Letter   A4

Extended regression models (ERMs)
Letter   A4

Finite mixture models (FMMs)
Letter   A4

Group sequential designs
Letter   A4

Interval-censored Cox model
Letter   A4

Item response theory (IRT)
Letter   A4

Lasso: Prediction and inference
Letter   A4

Latent class analysis (LCA)
Letter   A4

Maximum likelihood estimation
Letter   A4

Meta-analysis
Letter   A4

Multilevel mixed-effects models
Letter   A4

Multiple imputation
Letter   A4

Nonparametric regression
Letter   A4

Panel data
Letter   A4

Power, precision, and sample-size analysis
Letter   A4

PyStata
Letter   A4

Reproducible and automated reporting
Letter   A4

Reproducibility and backward compatibility
Letter   A4

Spatial autoregressivel (SAR) models
Letter   A4

Structural equation modeling (SEM)
Letter   A4

Survey data
Letter   A4

Survival analysis
Letter   A4

Time series
Letter   A4

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Resources



Programming with Stata
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Teaching with Stata
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Tech support
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Stata Press



Author Support Program
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Become a Stata Press® author
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Stata Journal



Publish in the Stata Journal
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The Stata Journal
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Supplementary materials