On panelView, and why we should always look at our data first.. yiqingxu.substack.com/p/plot-your-...

First real post. Still learning...

I started a substack... to jot down my thoughts and learning notes on causal inference, panel data, and AI --- and maybe a few other random events along the way.

Thanks, Anton, for the adverticement. We believe this is just one small attempt of combinine the (minimally) structural apporach with DML -- randomization is doing a lot of work here, too.

1/ Happy to release StatsClaw — an open-source multi-agent workflow for building statistical software with AI. w/ @tianzhuqin.bsky.social Site: statsclaw.ai Paper: bit.ly/statsclaw GitHub: github.com/statsclaw/

1/2 Important caveat: Computational reproducibility does not imply credibility, especially w.r.t. to research design. In the scientific literature, *reproducibility* means reproducing results using authors’ code and data, whereas *replication* goes beyond what was originally done or provided.

1/🧵 A major update to our paper: "Scaling Reproducibility" w/ Leo Yang [Cross-posted from X] We move beyond reanalyzing a single design to (almost) full-paper replication! Paper: bit.ly/repro-ai

1/ Sorry for double-posting from X. Sharing a new working paper for the Year of the Horce 🐎: "An AI-assisted workflow that scales reproducibility in empirical research" (bit.ly/repro-ai) w/ Leo Yang Yang

A blog post giving a more thorough take on survey experiments and the credibility revolution: cyrussamii.com?p=4168

Preliminary. Comments are welcome!

Thrilled to share that **fect** has won the 2025 Best Statistical Software Award from the Society of Political Methodology. We're honored! polmeth.org/statistical-... To celebrate, we've just released fect v2.0.5 on CRAN & Github 🎉

Honored by this recognition!

Glad this paper is finally out in @apsrjournal.bsky.social, five years after we began the project. Hope it proves useful to researchers. www.cambridge.org/core/journal... Software support: yiqingxu.org/packages/fec...

As a Chinese immigrant living in the U.S., I have witnessed declines in optimism purely due to the drastic erosion of existing institutions—first around 2015 in my home country, and now, in 2025, in the United States. It's devastating.

Draft “A Practical Guide to Estimating Conditional Marginal Effects: Modern Approaches” is on arXiv: arxiv.org/pdf/2504.01355 w/ two amazing grad students, Jiehan_Liu & Ziyi Liu 🧵

I woke up at 5:30 a.m. today, and the first thing on my mind was Stefan Zweig’s The World of Yesterday: Memoirs of a European, a book I loved as a child. His despair stuck with me and feels especially relevant now.

"The bottom 80% of earners spent 25% more than they did four years earlier, barely outpacing price increases of 21% over that period. The top 10% spent 58% more." www.wsj.com/economy/cons...

We streamlined six new DID-like estimators and created this tutorial for implementation in R. yiqingxu.org/packages/fec... Hope you no longer need to spend months figuring out what these estimators are and how to use them.

Looks like everyone is here 😆 1/ Recently, Professor Uri Simonsohn critiqued Hainmueller, Mummolo & Xu (2019), arguing that the proposed methods fail to recover the conditional marginal effect (CME): datacolada.org/121 We appreciate the critique and offer this response: arxiv.org/pdf/2502.05717 🧵