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作者：Liu, Hanzhong; Tu, Fuyi; Ma, Wei
作者单位：Tsinghua University; Renmin University of China
摘要：We consider the problem of estimating and inferring treatment effects in randomized experiments. In practice, stratified randomization, or more generally, covariate-adaptive randomization, is routinely used in the design stage to balance treatment allocations with respect to a few variables that are most relevant to the outcomes. Then, regression is performed in the analysis stage to adjust the remaining imbalances to yield more efficient treatment effect estimators. Building upon and unifying...
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作者：Wang, J.; Wang, H.; Cheng, K.
作者单位：University of Connecticut
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作者：Jiang, Zhichao; Chen, Shizhe; Ding, Peng
作者单位：Sun Yat Sen University; University of California System; University of California Davis; University of California System; University of California Berkeley
摘要：Point processes are probabilistic tools for modelling event data. While there exists a fast-growing literature on the relationships between point processes, how such relationships connect to causal effects remains unexplored. In the presence of unmeasured confounders, parameters from point process models do not necessarily have causal interpretations. We propose an instrumental variable method for causal inference with point process treatment and outcome. We define causal quantities based on p...
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作者：Zhou, Hang; Yao, Fang; Zhang, Huiming
作者单位：Peking University; University of Macau
摘要：Despite extensive studies on functional linear regression, there exists a fundamental gap in theory between the ideal estimation from fully observed covariate functions and the reality that one can only observe functional covariates discretely with noise. The challenge arises when deriving a sharp perturbation bound for the estimated eigenfunctions in the latter case, which renders existing techniques for functional linear regression not applicable. We use a pooling method to attain the estima...
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作者：Tudball, Matthew J.; Hughes, Rachael A.; Tilling, Kate; Bowden, Jack; Zhao, Qingyuan
作者单位：University of Bristol; University of Exeter; University of Cambridge
摘要：Many partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference in this general setting remains to be developed. To address this, we derive an asymptotically valid confidence interval for the optimal value through an appropriate relaxation of the estimated set. We then apply this general result to the problem of selecti...
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作者：Wang, Shulei
作者单位：University of Illinois System; University of Illinois Urbana-Champaign
摘要：Differential abundance tests for compositional data are essential and fundamental in various biomedical applications, such as single-cell, bulk RNA-seq and microbiome data analysis. However, because of the compositional constraint and the prevalence of zero counts in the data, differential abundance analysis on compositional data remains a complicated and unsolved statistical problem. This article proposes a new differential abundance test, the robust differential abundance test, to address th...
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作者：Guo, Kevin; Rothenhausler, Dominik
作者单位：Stanford University
摘要：In observational causal inference, exact covariate matching plays two statistical roles: (i) it effectively controls for bias due to measured confounding; (ii) it justifies assumption-free inference based on randomization tests. In this paper we show that inexact covariate matching does not always play these same roles. We find that inexact matching often leaves behind statistically meaningful bias, and that this bias renders standard randomization tests asymptotically invalid. We therefore re...
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作者：Kreiss, J. P.; Paparoditis, E.
作者单位：Braunschweig University of Technology; University of Cyprus
摘要：Fitting parametric models by optimizing frequency-domain objective functions is an attractive approach of parameter estimation in time series analysis. Whittle estimators are a prominent example in this context. Under weak conditions and the assumption that the true spectral density of the underlying process does not necessarily belong to the parametric class of spectral densities fitted, the distribution of Whittle estimators typically depends on difficult to estimate characteristics of the u...
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作者：Luo, Lan; Wang, Jingshen; Hector, Emily C.
作者单位：Rutgers University System; University of California System; University of California Berkeley; North Carolina State University
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作者：Schultheiss, C.; Buhlmann, P.
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：We present a new method for causal discovery in linear structural equation models. We propose a simple technique based on statistical testing in linear models that can distinguish between ancestors and non-ancestors of any given variable. Naturally, this approach can then be extended to estimating the causal order among all variables. Unlike with many methods, it is possible to provide explicit error control for false causal discovery, at least asymptotically. This holds even under Gaussianity...
