Category: Causal Inference

Gendered languages and women’s workforce participation rates

Rajesh Venkatachalapathy writes: I recently came across a world bank document claiming that gendered languages reduce women’s labor force participation rates. It is summarized in the following press release: Gendered Languages May Play a Role in Limiting Women’s Opportunities, New Research Finds. This sounds a lot like the piranha problem, if there is any effect […]

Causal inference using repeated cross sections

Sadish Dhakal writes: I am struggling with the problem of conditioning on post-treatment variables. I was hoping you could provide some guidance. Note that I have repeated cross sections, not panel data. Here is the problem simplified: There are two programs. A policy introduced some changes in one of the programs, which I call the […]

“Did Austerity Cause Brexit?”

Carsten Allefeld writes: Do you have an opinion on the soundness of this study by Thiemo Fetzer, Did Austerity Cause Brexit?. The author claims to show that support for Brexit in the referendum is correlated with the individual-level impact of austerity measures, and therefore possibly caused by them. Here’s the abstract of Fetzer’s paper: Did […]

“Did Austerity Cause Brexit?”

Carsten Allefeld writes: Do you have an opinion on the soundness of this study by Thiemo Fetzer, Did Austerity Cause Brexit?. The author claims to show that support for Brexit in the referendum is correlated with the individual-level impact of austerity measures, and therefore possibly caused by them. Here’s the abstract of Fetzer’s paper: Did […]

Causal inference with time-varying mediators

Adan Becerra writes to Tyler VanderWeele: I have a question about your paper “Mediation analysis for a survival outcome with time-varying exposures, mediators, and confounders” that I was hoping that you could help my colleague (Julia Ward) and me with. We are currently using Medicare claims data to evaluate the following general mediation among dialysis […]

How to simulate an instrumental variables problem?

Edward Hearn writes: In an effort to buttress my own understanding of multi-level methods, especially pertaining to those involving instrumental variables, I have been working the examples and the exercises in Jennifer Hill’s and your book. I can find general answers at the Github repo for ARM examples, but for Chapter 10, Exercise 3 (simulating […]

Causal inference: I recommend the classical approach in which an observational study is understood in reference to a hypothetical controlled experiment

Amy Cohen asked me what I thought of this article, “Control of Confounding and Reporting of Results in Causal Inference Studies: Guidance for Authors from Editors of Respiratory, Sleep, and Critical Care Journals,” by David Lederer et al. I replied that I liked some of their recommendations (downplaying p-values, graphing raw data, presenting results clearly) […]

“Did Jon Stewart elect Donald Trump?”

I wrote this post a couple weeks ago and scheduled it for October, but then I learned from a reporter that the research article under discussion was retracted, so it seemed to make sense to post this right away while it was still newsworthy. My original post is below, followed by a post script regarding […]

Did blind orchestra auditions really benefit women?

You’re blind! And you can’t see You need to wear some glasses Like D.M.C. Someone pointed me to this post, “Orchestrating false beliefs about gender discrimination,” by Jonatan Pallesen criticizing a famous paper from 2000, “Orchestrating Impartiality: The Impact of ‘Blind’ Auditions on Female Musicians,” by Claudia Goldin and Cecilia Rouse. We’ve all heard the […]

Difference-in-difference estimators are a special case of lagged regression

Fan Li and Peng Ding write: Difference-in-differences is a widely-used evaluation strategy that draws causal inference from observational panel data. Its causal identification relies on the assumption of parallel trend, which is scale dependent and may be questionable in some applications. A common alternative method is a regression model that adjusts for the lagged dependent […]

Continuing discussion of status threat and presidential elections, with discussion of challenge of causal inference from survey data

Last year we reported on an article by sociologist Steve Morgan, criticizing a published paper by political scientist Diana Mutz. A couple months later we updated with Mutz’s response to Morgan’s critique. Finally, Morgan has published a reply to Mutz’s response to Morgan’s comments on Mutz’s paper. Here’s a passage that is of methodological interest: […]

Automatic voter registration impact on state voter registration

Sean McElwee points us to this study by Kevin Morris and Peter Dunphy, who write: Automatic voter registration or AVR . . . features two seemingly small but transformative changes to how people register to vote: 1. Citizens who interact with government agencies like the Department of Motor Vehicles are registered to vote, unless they […]