Chuck Huber, StataCorpCausal Mediation Analysis Using StataObservational studies try to determine the effect of an exposure or treatment (T) on an outcome of interest (Y). Sometimes the treatment has an effect on a third variable, called a mediating variable (M), which also influences the outcome. So the treatment may have both a direct effect on the outcome (T -> Y) and an indirect effect on the outcome through its influence on the mediating variable (T -> M -> Y). The goal of causal mediation analysis is to identify and quantify these direct and indirect effects. This presentation will introduce the concepts and jargon of causal mediation analysis, demonstrate how to analyze these kinds of studies, and show how to interpret and visualize these kinds of relationships in clinical trials.Click here for presentation slides Watch presentation |
Luis Eduardo San Martin, World BankEnsuring Reproducibility in Stata: Insights From the World Bank's Reproducible Research RepositoryThe challenge of reproducing economics research has gained increased attention with the growing advocacy for open science in the field. Economics journals and research institutions are quickly adopting reproducibility guidelines, requiring authors to provide code and data for reproducing results and ensuring the trustworthiness of their findings. Presented by the Development Impact Analytics team of the World Bank, this session delves into the intricacies of achieving reproducibility in Stata works. Since the launch of the World Bank's Reproducible Research Repository, the team has conducted reproducibility verifications and curated reproducibility packages for almost a two hundred working papers and reports from diverse research teams in the organization, building up a valuable and novel experience into addressing common issues that break reproducibility in Stata analyses. The session will present an overview of the workflows and tools the team has developed in response to identified reproducibility challenges in typical Stata works, covering key topics such as controlling the versions of external dependencies and appropriately handling randomness in Stata code. The presentation will include practical strategies for enhancing the transparency and reliability of Stata-based research.Click here for presentation slides Watch presentation |
Fernando Rios-AvilaJWDIDThis presentation explores Fernando's perspective on Jeff Wooldridge's DID approach, incorporating his latest Flex method. Additionally, this paper includes modifications Fernando and his coauthors developed for gravity modelsLinks
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Davud Rostam-Afschar, University of MannheimHow to Run Adaptive Experiments in Stata: Causal Inference from Multi-Armed BanditsThis talk provides an introduction to batched bandit experiments. We will discuss how to simulate, interactively run, and analyze batched bandit experiments using the Stata program bbandits. We will discuss results from Monte Carlo simulations and study how to obtain valid statistical inference and correct coverage and discuss a wide range of statistics and illustrations to analyze adaptively collected data. The objective is to learn how to implement own batched bandit experiments.Click here for presentation slides. Watch presentation |
Amy Grant and David White, SDASBe Bold: Use the Open-Source Features of Stata to Customise Commands to Suit Your NeedsIn this paper Amy and David will present approaches that can be used by Stata users to customise both Stata and user-written commands to suit their specific needs. Amy and David have been helping customers with Stata at SDAS and have seen many customers interested in user-written commands or changing approaches to Stata commands, however, not quite able to.Click here for presentation slides. Watch presentation |
James Hurley, University of MelbourneVisualising and Diagnosing Spillover Within Randomised Controlled Trials Using Diagnostic Test Assessment Methods in StataThe presentation will demonstrate the use of Stata to visualize and diagnose spillover within randomized controlled trials. In the past, techniques such as the L’abbe plot might have been used but the plots available with diagnostic test assessment methods in Stata [user written commands] are better. Spillover is crucial for the inference from RCT’s but difficult to demonstrate without use of information from outside the RCT. The data [plots and Stata codes] are available are Hurley JC. Visualizing and diagnosing spillover within randomized concurrent controlled trials through the application of diagnostic test assessment methods. BMC Medical Research Methodology. 2024 Aug 16;24(1):182.Click here for the presentation slides. Watch presentation |
John Kane, New York UniversitySharing Stata Knowledge Online: Existing Examples and Guidance on How to Do It More EffectivelyLearning and sharing Stata knowledge online can be a challenging endeavor, especially when it comes to data visualization. In his presentation, John will cover some existing resources for doing so, including the notable advantages offered by the website Medium. John will demonstrate how Stata users can use Medium—and its popular “Stata Gallery”—to learn, or share their own, valuable insights for making more effective visualizations, communicating key statistical concepts, and doing better analysis.Click here for presentation slides. Watch presentation |
Jan Kabatek, University of MelbourneSingle Precision Storage Default - Is It Time to Bid FarewellThis presentation highlights another legacy issue that characterizes the current versions of Stata. The issue is that, by default, Stata stores data in single-precision data format, but performs all the calculations in double precision. When handling non-integer data, this gives rise to unexpected behaviors that undermine the presumed functions of basic logical operators (“==”, “!=”, “<”, and “>”). Jan will give several examples to illustrate the ways in which this behavior can fundamentally alter the conclusions of statistical models. Similar to his earlier presentation, this one is intended to start the conversation whether it is time to move away from the single-precision storage default, fully embracing the double-precision format.Click here for presentation slides. Watch presentation |
Wong Keng Siong, DBS BankPast Sovereign Defaults as a Predictor of Future DefaultsThis study looks at the likelihood that a country that defaults once would default again by testing the statistical significance between sovereign default as dependent variable against lags of itself as the independent variable. Set up panelised probit models with Stata, the results show that a sovereign that has defaulted is very likely to default again in the next eight years following the initial default.Watch presentation |
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