Yu‐Chin Hsu
Biographic Data
| ID | 5986706 |
|---|---|
| NAME | Yu‐Chin Hsu |
| GIVEN NAMES | Yu‐Chin |
| FAMILY NAME | Hsu |
| SIGNATURE | HSU Y C |
| AFFILIATIONS | National Central University |
| VERIFIED | No |
| TOTAL WORKS | 9 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 9 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Testing Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data
We propose a Cramér–von Mises–type test for testing whether the mean potential outcome given a specific treatment level has a weakly monotonic relationship with the continuous treatment under unconfoundedness. To flexibly control for a possibly high-dimensional set of covariates, our test is based on a double debiased machine learning method. We show that our test controls asymptotic size and is consistent against any fixed alternative. We apply …
Quantile Policy Effects
To assess the dynamic distributional impacts of macroeconomic policy, we propose quantile policy effects to quantify disparities between the quantiles of potential outcomes under different policies. We first identify quantile policy effects under the unconfoundedness assumption and propose an inverse probability weighting estimator. We then examine the asymptotic behavior of the proposed estimator in a time series framework and suggest a blockwis…
Counterfactual Treatment Effects
This article proposes statistical methods to evaluate the quantile counterfactual treatment effect (QCTE) if one were to change the composition of the population targeted by a status quo program. QCTE enables a researcher to carry out an ex-ante assessment of the distributional impact of certain policy interventions or to investigate the possible explanations for treatment effect heterogeneity. Assuming unconfoundedness and invariance of the cond…
Estimation of Conditional Average Treatment Effects With High-Dimensional Data
Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE …
A multilevel analysis of the determinants of the attitude toward separate cycle paths in Taiwan
Objective This study examines why people support a policy for separate cycle paths but oppose the necessary means for its implementation—the narrowing of vehicle lanes and reduction of roadside parking spaces. Methods This study used cross‐sectional data from the 2009 Taiwan Bicycle Use Survey, covering 23 counties ( n = 9236) and applying multilevel multinomial logistic regression models. Results Not in my backyard (NIMBY) or not in my interest …
Testing monotonicity of conditional treatment effects under regression discontinuity designs
Researchers are often interested in the relationship between treatment effects and observed individual heterogeneity. This paper proposes the first nonparametric monotonicity test for conditional treatment effects under the popular regression discontinuity framework. The proposed test examines whether the average treatment effect or the local average treatment effect has a monotonic relationship with some of the observed individual characteristic…
Direct and indirect effects of continuous treatments based on generalized propensity score weighting
This paper proposes semi‐ and nonparametric methods for disentangling the total causal effect of a continuous treatment on an outcome variable into its natural direct effect and the indirect effect that operates through one or several intermediate variables called mediators jointly. Our approach is based on weighting observations by the inverse of two versions of the generalized propensity score (GPS), namely the conditional density of treatment …
A Stochastic Frontier Model with Endogenous Treatment Status and Mediator
Government policies are frequently used to promote productivity. Some policies are designed to enhance production technology, while others are meant to improve production efficiency. An important issue to consider when designing and evaluating policies is whether a mediator is required or effective in achieving the desired final outcome. To better understand and evaluate the policies, we propose a new stochastic frontier model with a treatment st…
Estimating Conditional Average Treatment Effects
We consider a functional parameter called the conditional average treatment effect (CATE), designed to capture heterogeneity of a treatment effect across subpopulations when the unconfoundedness assumption applies. In contrast to quantile regressions, the subpopulations of interest are defined in terms of the possible values of a set of continuous covariates rather than the quantiles of the potential outcome distributions. We show that the CATE p…
No prominent works on this page.
Estimating Conditional Average Treatment Effects
We consider a functional parameter called the conditional average treatment effect (CATE), designed to capture heterogeneity of a treatment effect across subpopulations when the unconfoundedness assumption applies. In contrast to quantile regressions, the subpopulations of interest are defined in terms of the possible values of a set of continuous covariates rather than the quantiles of the potential outcome distributions. We show that the CATE p…
Direct and indirect effects of continuous treatments based on generalized propensity score weighting
This paper proposes semi‐ and nonparametric methods for disentangling the total causal effect of a continuous treatment on an outcome variable into its natural direct effect and the indirect effect that operates through one or several intermediate variables called mediators jointly. Our approach is based on weighting observations by the inverse of two versions of the generalized propensity score (GPS), namely the conditional density of treatment …
A Stochastic Frontier Model with Endogenous Treatment Status and Mediator
Government policies are frequently used to promote productivity. Some policies are designed to enhance production technology, while others are meant to improve production efficiency. An important issue to consider when designing and evaluating policies is whether a mediator is required or effective in achieving the desired final outcome. To better understand and evaluate the policies, we propose a new stochastic frontier model with a treatment st…
Testing monotonicity of conditional treatment effects under regression discontinuity designs
Researchers are often interested in the relationship between treatment effects and observed individual heterogeneity. This paper proposes the first nonparametric monotonicity test for conditional treatment effects under the popular regression discontinuity framework. The proposed test examines whether the average treatment effect or the local average treatment effect has a monotonic relationship with some of the observed individual characteristic…
Counterfactual Treatment Effects
This article proposes statistical methods to evaluate the quantile counterfactual treatment effect (QCTE) if one were to change the composition of the population targeted by a status quo program. QCTE enables a researcher to carry out an ex-ante assessment of the distributional impact of certain policy interventions or to investigate the possible explanations for treatment effect heterogeneity. Assuming unconfoundedness and invariance of the cond…
Estimation of Conditional Average Treatment Effects With High-Dimensional Data
Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE …
A multilevel analysis of the determinants of the attitude toward separate cycle paths in Taiwan
Objective This study examines why people support a policy for separate cycle paths but oppose the necessary means for its implementation—the narrowing of vehicle lanes and reduction of roadside parking spaces. Methods This study used cross‐sectional data from the 2009 Taiwan Bicycle Use Survey, covering 23 counties ( n = 9236) and applying multilevel multinomial logistic regression models. Results Not in my backyard (NIMBY) or not in my interest …
Quantile Policy Effects
To assess the dynamic distributional impacts of macroeconomic policy, we propose quantile policy effects to quantify disparities between the quantiles of potential outcomes under different policies. We first identify quantile policy effects under the unconfoundedness assumption and propose an inverse probability weighting estimator. We then examine the asymptotic behavior of the proposed estimator in a time series framework and suggest a blockwis…
Testing Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data
We propose a Cramér–von Mises–type test for testing whether the mean potential outcome given a specific treatment level has a weakly monotonic relationship with the continuous treatment under unconfoundedness. To flexibly control for a possibly high-dimensional set of covariates, our test is based on a double debiased machine learning method. We show that our test controls asymptotic size and is consistent against any fixed alternative. We apply …
Econometrics (7 works) · Advanced Causal Inference Techniques (6 works) · Economics (6 works) · Mathematics (6 works) · Statistical Methods and Inference (6 works) · Statistics (6 works) · Computer Science (4 works) · Estimator (4 works) · Average treatment effect (3 works) · Medicine (3 works)