Explore treatment-effect heterogeneity without parametric assumptions. Import Parquet files directly and more in the latest StataNow update. Dive deeper into H2O. Interface with today’s most popular AI engines.
And more. All inside the latest issue of the Stata News.
Explore treatment-effect heterogeneity without parametric assumptions. Import Parquet files directly and more in the latest StataNow update. Dive deeper into H2O. Interface with today’s most popular AI engines.
And more. All inside the latest issue of the Stata News.
Conditional average treatment effects (CATEs) allow researchers to study heterogeneous treatment effects, and Stata's new cate command provides the flexibility for fitting these models without parametric assumptions. In this spotlight, we demonstrate how using random forest to estimate CATEs can reveal effects missed by the parametric approach.
Import Parquet data directly using the new import parquet command. Fit causal mediation models with two mediators—either sequential or parallel—with mediate. Compute quantiles in Mata using the new quantile() function. And more.
Have you used our new h2oml suite for machine learning in Stata yet? Ready for more? We’ve released three new postestimation commands and three tutorial blogs to expand your skills, enhance statistical rigor, and uncover new insights.
Ready to level up your Stata and Python skills? Learn how easy it is to write Stata commands that use PyStata to interface with today’s most popular AI engines.
Dr. Marvin Hanisch’s YouTube channel, Statistics Made Easy, offers intuitive tutorials in Stata on topics from data preparation and descriptive statistics to regression, mediation, and moderation.
Revisit the presentations you enjoyed or catch up on anything you missed. Access all the great material shared by leading economists using Stata from this year's Economics Virtual Symposium.
