Zhexiao Lin

PhD student in Statistics @UCBerkeley @UCBStatistics | Causal inference, Graph-based methods, Econometrics | Previously @UWStat

[Zhexiao Lin @zzzxlin]

Any comments are welcome

[Zhexiao Lin @zzzxlin]

Excited to share a new paper with @pengding00 and Peter Bickel on combining estimators. https://t.co/BdDDWESEfe

TL;DR: When combining estimators, how can we do valid inference when the bias is unknown? [1/2]

[Zhexiao Lin @zzzxlin]

We propose a strategy to combine estimators from a sensitivity analysis perspective. We construct a sequence of confidence intervals indexed by the magnitude of bias and propose the b-value to quantify the maximum bias so that combining estimators yields an insignificant result

[Zhexiao Lin @zzzxlin]

Excited to share a new work with @pengding00 on causal inference in time-series experiments.
Appreciate any comments or feedback

[Econometrics Papers @eBlogs]

Unifying regression-based and design-based causal inference in time-series experiments. https://t.co/et712e7BxX

[Peng Ding @pengding00]

excited to see the published version of our paper on dealing with missing covariates and outcomes in randomized trials: https://t.co/4uts235hQX It is a simple paper with some intriguing results. Slides are here: https://t.co/VEL3gPA4bS @FanLiDuke

[Triad sou. @triadsou]

On Rosenbaum’s Rank-based Matching Estimator. Matias D Cattaneo, Fang Han, Zhexiao Lin. Biometrika. https://t.co/uYwNBXg4ul

[Zhexiao Lin @zzzxlin]

@johnleibniz Fantastic!🤩

[Fang Han @johnleibniz]

We give the closed-form expression for the asymptotic variance of the Abadie-Imbens matching estimator.

[Ziyao Shangguan @ZiyaoShangguan]

We are thrilled to introduce 🍅 TOMATO - a novel visual temporal reasoning benchmark! Building on top of existing benchmarks that primarily focus on reasoning about the order of different events or retrieving static details among frames, TOMATO emphasizes the necessity of reasoning across all frames as a continuous sequence, where individual frames alone are insufficient or misleading for understanding video contents.

📄 Paper: https://t.co/MDGArS3Pup
🤗 Data: https://t.co/61tRjVdRdZ
💻 Code: https://t.co/4w89JaYZP1

🧵1/9

[Fang Han @johnleibniz]

We prove that the Abadie-Imbens matching estimator is bootstrap consistent—as long as you scale up the number of matches.

[Fang Han @johnleibniz]

This paper (https://t.co/OO5va89tAd) will appear in 𝗕𝗶𝗼𝗺𝗲𝘁𝗿𝗶𝗸𝗮. It analyzes NN matching when using rank-based distances.

[Yiqing Xu @xuyiqing]

Sharing a new working paper with Anqi Zhao & Peng Ding @pengding00, titled "Factorial Difference-in-Differences." https://t.co/29bDeAlu3J 🧵

Comments and suggestions are welcome!

[Zhexiao Lin @zzzxlin]

@willcb Interested!

[Zhexiao Lin @zzzxlin]

@jinghuai_zhang congrats!!!nb🐮

[Peng Ding @pengding00]

excited to see the physical copy of the book; nervous about the potential errors. Comments are welcome.

[Zhexiao Lin @zzzxlin]

Excited to share my first paper in applications, where we applied the graph neural networks to analyze scRNA-seq data. Very happy to be advised by Wei @weisun2013 on this project!

https://t.co/pFvSUifK7B

[Fang Han @johnleibniz]

My lecture notes on permutation processes

https://t.co/gzXl9iqs9P

[Fang Han @johnleibniz]

This paper is going to appear in a future issue of the Annals of Applied Probability; it gives the limiting distribution of Chatterjee’s correlation when the data are supported over a manifold.

https://t.co/lrlPdMYRck

[Fang Han @johnleibniz]

A simpler example is Kendall's tau, for which the U-statistic version is more efficient than the sample-mean version (centralize using the pop mean). However, the same observation does not apply to Pearson's correlation, for which if you know the pop mean, you should use it.

[Peng Ding @pengding00]

IPW with the estimated propensity score is another example. The first-stage estimation reduces the asymptotic variance, which surprises many people. A recent paper is https://t.co/PvzcZV5HGO Also, Newey&McFadden chapter 6 is about "two-step estimation" https://t.co/h40yljXwYv