Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Oka, Tatsushi"'
We propose a novel regression adjustment method designed for estimating distributional treatment effect parameters in randomized experiments. Randomized experiments have been extensively used to estimate treatment effects in various scientific fields
Externí odkaz:
http://arxiv.org/abs/2407.16037
In this paper, we address the issue of estimating and inferring the distributional treatment effects in randomized experiments. The distributional treatment effect provides a more comprehensive understanding of treatment effects by characterizing het
Externí odkaz:
http://arxiv.org/abs/2407.14074
Recent models for natural language understanding are inclined to exploit simple patterns in datasets, commonly known as shortcuts. These shortcuts hinge on spurious correlations between labels and latent features existing in the training data. At inf
Externí odkaz:
http://arxiv.org/abs/2406.12060
Macro variables frequently display time-varying distributions, driven by the dynamic and evolving characteristics of economic, social, and environmental factors that consistently reshape the fundamental patterns and relationships governing these vari
Externí odkaz:
http://arxiv.org/abs/2403.12456
Excellent tail performance is crucial for modern machine learning tasks, such as algorithmic fairness, class imbalance, and risk-sensitive decision making, as it ensures the effective handling of challenging samples within a dataset. Tail performance
Externí odkaz:
http://arxiv.org/abs/2306.05292
Vector autoregression is an essential tool in empirical macroeconomics and finance for understanding the dynamic interdependencies among multivariate time series. In this study, we expand the scope of vector autoregression by incorporating a multivar
Externí odkaz:
http://arxiv.org/abs/2303.04994
We study the estimation of heterogeneous effects of group-level policies, using quantile regression with interactive fixed effects. Our approach can identify distributional policy effects, particularly effects on inequality, under a type of differenc
Externí odkaz:
http://arxiv.org/abs/2208.03632
We propose a semiparametric method to estimate the average treatment effect under the assumption of unconfoundedness given observational data. Our estimation method alleviates misspecification issues of the propensity score function by estimating the
Externí odkaz:
http://arxiv.org/abs/2206.08503
Understanding variable dependence, particularly eliciting their statistical properties given a set of covariates, provides the mathematical foundation in practical operations management such as risk analysis and decision-making given observed circums
Externí odkaz:
http://arxiv.org/abs/2203.12228
Governments around the world have implemented preventive measures against the spread of the coronavirus disease (COVID-19). In this study, we consider a multivariate discrete-time Markov model to analyze the propagation of COVID-19 across 33 provinci
Externí odkaz:
http://arxiv.org/abs/2008.06051