AI and economics research
To date, I've seen a lot more on how AI can help revolutionize economics research than I've seen how concretely AI made new research happen in economics.
In my own experience, I've had Claude Code markedly increase my productivity by cutting down on coding time. But it has failed to give me a single new economic insight. (I've tried.)
I’m on the academic job market this fall! I study comparative political economy, state-business relations, social policy, and China. Learn more about my work here: https://t.co/JI2CCfRyn7
#FirstView from @polanalysis -
Estimating the Local Average Treatment Effect Without the Exclusion Restriction - https://t.co/YOYLlwkppU
- Zachary Markovich
@DanBischof @jeffreymziegler@seanjwestwood It’s framed more generally than earlier versions (which look directly at fmc’s) but the running example uses one
fwiw, the idea in this img was super useful for my learning
causal inference requires both identification *and* estimation of an estimand (AKA functional, parameter)
you can (and should!) think of them separately, but you'll want to make good decisions on both sides to succeed
In social science, I feel like we treat coding errors as a personal failing, but they're inevitable for anyone doing that much programing. We should institutionalize practices (like code reviews/ unit testing) that help people find the mistakes before they're published.
I've been asked what specific local & state elections matter a lot in the wake of Dobbs, & what concretely they'd affect.
With the caveats that the next steps need to involve mobilization, solidarity, & protests that aren't electorally focused, here are some thoughts. ↓
The JOP blog has a piece about our forthcoming paper (with @zachmarkovich). The title pretty much gives you the punch line, but click through for more details!
🚨BLOGPOST ALERT🚨
Increasing the minimum wage spurs political participation of affected workers. @zachmarkovich and @ArielRWhite compare voting rates of New York City employees before and after minimum wage increases. Read what they find on our blog⬇️
https://t.co/9iJ0HA8HeA
Very important research by former colleagues at @MITPoliSci examining mental health among grad students in top PhD programs. Departments should take these findings seriously and act accordingly.
My paper at #NeurIPS2021 this week introduces a more nuanced approach which explicitly defines causal effects in terms of the complete bundle of causal variables. If you work with these sorts of complex variables, please check out the preprint at https://t.co/n5lfbph6MO
Complex traits like democracy, race, or national power typically enter political scientists' models as single variables. Such approaches are inherently reductive and risk understating the magnitude of causal effects because they discard so much of the variation in these traits.