Treatment doesn’t always go from 0 → 1 and stay there. With 𝘅𝘁𝘀𝘄𝗶𝘁𝗰𝗵𝗱𝗶𝗱 in StataNow, estimate DID event-study effects when treatments have multiple levels or groups can switch in and out of treatment. Learn more: stata.com/statanow/DID-with-…

What if #meta-analysis is not just for combining study results, but for explaining why firm-level parameters differ in the first place? Maria Elena Bontempi (Univ. Bologna) repurposes @Stata's multivariate meta-analysis toolkit: estimating first firm-specific leverage-adjustment dynamics, then using meta-regression as a second-stage model to explain that heterogeneity via firm characteristics & debt covenants, all whilst handling correlated outcomes and estimation uncertainty, without the overparameterization of pooled interaction models. Presentation available @ bit.ly/4hcBQ1M

National averages hide the story — smoking and risky drinking don't look the same in Puglia as they do in Trentino! Giovanni Capelli et al. (Istituto Superiore di Sanità) map Italy's regional health divides using PASSI surveillance data: weighted regional prevalences via Stata's svycommand, visualized through thematic maps with quintile-based classificationinstead of fixed thresholds — sharpening geographic contrast and revealing territorial inequalities that national benchmarks tend to mask. Read more at bit.ly/4ha1qVn

Raw Kaplan-Meier curves can be misleading in observational studies: a treatment group might just look worse because it's older or sicker at baseline, not because the treatment itself is harmful. Rino Bellocco (Univ. Milano-Bicocca & Karolinska) currently walking us through fixing this in Stata: from unadjusted KM curves, to Cox-adjusted hazard ratios, to fully standardized survival curves that compare "what if everyone got treated" vs. "what if no one did" — giving a real, interpretable causal contrast on the survival scale, not just a hazard ratio. Presentation available at bit.ly/4yO2Vjf

Not everyone responds to treatment in the same way — so why settle for one average effect? Di Liu (StataCorp) shows how to put @Stata 19's new cate command to full use: test for treatment-effect heterogeneity, see how effects vary by covariates or groups, uncover hidden subgroups in your data, and identify the best treatment-assignment rule. Learn more at bit.ly/4Akc4Bz

How does one allocate a treatment when your budget only covers part of the population and you cannot leave key subgroups untreated? Giovanni Cerulli (CNR-IRCrES) illustrating in @Stata how to learn optimal treatment-assignment policies under budget and coverage constraints: threshold-based rules, welfare-maximizing allocation, and practical algorithms to get as close to optimal as possible in real data. More @ bit.ly/4xA0RKH

Andrei Sirchenko, Bînzari & Wrebiak’s new @Stata command catmetrics offers 300+ measures of association, similarity & forecast evaluation for categorical data: one command instead of hunting down dozens of reinvented metrics across fields. Read the presentation at bit.ly/3UUSRpX

New @Stata community written post-estimation command: stfform from Daniele Spinelli & Rino Bellocco. Tests functional form adequacy of continuous covariates in Cox PH models via cumulative martingale residual sums (Lin, Wei & Ying 1993, Biometrika). Download in Stata using ssc install stfform. Presentation at bit.ly/3V1wIpU

Marta Ponzano currently presenting a new @Stata command: wqsreg (Ponzano et al., Eur J Epidemiol 2026): the first native Stata tool for Weighted Quantile Sum regression estimating joint + individual effects of correlated mixtures. wqsreg supports linear/logistic/Poisson models. Install wqsreg from GitHub: PonzanoMarta/wqsreg - Talk available @ bit.ly/4ybSTbu

Need to keep track of what’s in your @Stata ado paths? Then Jan Ditzen’s Stata command suite adotools is JUST what you need! adotools adds three commands to Stata: adodefine, adoadd and adoclear, which help to manage custom ado-file paths without editing adopath by hand! Install adotools from GitHub: github.com/JanDitzen/adotool…. Presentation @ bit.ly/4yIpG8e

Giovanni Cerulli (CNR-IRCrES) currently illustrating how to build lightweight #Python GUIs that talk directly to @Stata — turning complex do-file workflows into intuitive point-and-click desktop apps. Architecture, implementation & extensions covered. More at bit.ly/4h6ekna #Stata #Python"

Carlo Drago currently illistrating how to map the thematic structure of energy policy literature, combining @Stata & #Python for keyword extraction, PCA, K-means clustering & co-occurrence network analysis. Offering a transparent, reproducible bibliometric workflow, end to end. 👇 Talk available at bit.ly/46v3guY #Stata #Bibliometrics"

"Kristin MacDonald (@Stata) takes SEM beyond the basics, "getting under the hood" to exploring Stata 19's GSEM command for multilevel models, categorical latent variables & mixed outcome types. Compare params across groups, vary path models across unobserved classes & more 👇 bit.ly/46v3guY #SEM #Stata"

Marianna Nitti currently illustrating how her @stata community command rdlasso, developed with Marco Ventura, enables #stata users to include highdimensional covariates in Regression Discontinuity Design (#RDD) settings. rdlasso automates covariate selection using #Lasso-based procedures, supports both sharp and fuzzy settings and integrates seamlessly with rdrobust for bandwidth selection and inference. Their command relies on Stata’s native implementation of lasso for high-dimensional covariate selection and on rdrobust for bandwidth selection, estimation, and inference. In-depth presentation at: bit.ly/3TvEat0

Working with #experimentaldata? Then check out Davud Rostam-Afschare & Jan Kemper new @stata command bbandits, which offers #Stata users an exciting new tool for designing experiments via simulation, running interactive bandit experiments and implementing and analyzing adaptively collected data. Presentation and command details at bit.ly/3USO2xq

Need to analyse #threshold #regressions in #paneldata with large N and T and #interactive #fixed #effects? Then check out Jan Ditzen et al.'s latest community written @stata command xtthreshold! Detailed presentation at bit.ly/4xTgihU.

fffurtest: Giovanni Bruno and Chiara Oldani's new @stata community command allows #Stata users to implement #unit-root and #stationarity tests with smooth breaks approximated by flexible #Fourier forms. Go to bit.ly/4iuphBr for more info and to access the command