Yu‐Chin Hsu

Statistics and Probability · 2009–2019

A researcher in the Science corpus: 7 papers; trusts 62 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 98 citations into it; #3,466 of 12,932 by trust here, #4,964 by citations
• Economics and Econometrics — 48 citations into it; #6,653 of 23,481 by trust here, #11,156 by citations
• Causal Inference & Discovery — 22 citations into it
• Finance — 16 citations into it; #6,753 of 10,518 by trust here, #5,938 by citations
• Management Science and Operations Research — 13 citations into it
• Statistical Learning Theory — 7 citations into it
• Gender Studies — 6 citations into it
• Sociology and Political Science — 6 citations into it; #14,121 of 22,756 by trust here, #21,677 by citations

Whom Yu‐Chin Hsu trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Guido W. Imbens
2. Jinyong Hahn
3. Victor Chernozhukov
4. Alberto Abadie
5. Whitney K. Newey
6. Markus Frölich
7. James M. Robins
8. P. R. Hansen
9. Hidehiko Ichimura
10. Keisuke Hirano

Who trusts Yu‐Chin Hsu overall

1. Pedro H. C. Sant’Anna
2. Stephen G. Donald
3. Victor Chernozhukov
4. Matt Goldman
5. Michael Zimmert
6. Robert P. Lieli
7. Nathan Kallus
8. S. Lee
9. Jason Abrevaya
10. Qingliang Fan

Papers from Yu‐Chin Hsu's perspective

Ordered by how strongly the researchers Yu‐Chin Hsu trusts endorse each paper. Same corpus, a different order for every researcher.

1. The central role of the propensity score in observational studies for causal effects
2. The asymptotic variance of semiparametric estimators
3. Efficient Estimation of Models with Conditional Moment Restrictions Containing Unknown Functions
4. Double/debiased machine learning for treatment and structural parameters
5. On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects
6. Semiparametric instrumental variable estimation of treatment response models
7. Local polynomial modelling and its applications
8. LARGE SAMPLE PROPERTIES OF GENERALIZED METHOD OF
9. Regression Shrinkage and Selection via the Lasso
10. Efficient estimation of average treatment effects using the estimated propensity score

Their papers

• Estimation of Conditional Average Treatment Effects With High-Dimensional Data (2019) — 135 citations
• Estimating Conditional Average Treatment Effects (2014) — 210 citations
• Estimation and inference for distribution functions and quantile functions in treatment effect models (2014) — 94 citations
• Assessing value at risk with CARE, the Conditional Autoregressive Expectile models (2009) — 225 citations
• Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT (2014) — 60 citations
• Improving the Power of Tests of Stochastic Dominance (2016) — 68 citations
• Testing the Predictive Ability of Technical Analysis Using a New Stepwise Test Without Data Snooping Bias (2010) — 171 citations