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作者：Eckles, Dean; Ignatiadis, Nikolaos; Wager, Stefan; Wu, Han
作者单位：Massachusetts Institute of Technology (MIT); University of Chicago; University of Chicago; Stanford University
摘要：Regression discontinuity designs assess causal effects in settings where treatment is determined by whether an observed running variable crosses a prespecified threshold. Here, we propose a new approach to identification, estimation and inference in regression discontinuity designs that uses knowledge about exogenous noise (e.g., measurement error) in the running variable. In our strategy, we weight treated and control units to balance a latent variable, of which the running variable is a nois...
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作者：Khan, S.; Ugander, J.
作者单位：Stanford University; Stanford University
摘要：A popular method for variance reduction in causal inference is propensity-based trimming, the practice of removing units with extreme propensities from the sample. This practice has theoretical grounding when the data are homoscedastic and the propensity model is parametric (Crump et al., 2009; Yang & Ding, 2018), but in modern settings where heteroscedastic data are analysed with nonparametric models, existing theory fails to support current practice. In this work, we address this challenge b...
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作者：Agnoletto, D.; Rigon, T.; Dunson, D. B.
作者单位：Duke University; University of Milano-Bicocca
摘要：Generalized linear models are routinely used for modelling relationships between a response variable and a set of covariates. The simple form of a generalized linear model comes with easy interpretability, but also leads to concerns about model misspecification impacting inferential conclusions. A popular semiparametric solution adopted in the frequentist literature is quasilikelihood, which improves robustness by only requiring correct specification of the first two moments. We develop a robu...
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作者：Davis, Richard A.; Fernandes, Leon
作者单位：Columbia University
摘要：A fundamental and often final step in time series modelling is to assess the quality of fit of a proposed model to the data. Since the underlying distribution of the innovations that generate a model is often not prescribed, goodness-of-fit tests typically take the form of testing the fitted residuals for serial independence. However, these fitted residuals are intrinsically dependent since they are based on the same parameter estimates, and thus standard tests of serial independence, such as ...
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作者：Loyal, Joshua D.; Chen, Yuguo
作者单位：State University System of Florida; Florida State University; University of Illinois System; University of Illinois Urbana-Champaign
摘要：Latent space models are often used to model network data by embedding a network's nodes into a low-dimensional latent space; however, choosing the dimension of this space remains a challenge. To this end, we begin by formalizing a class of latent space models we call generalized linear network eigenmodels that can model various edge types (binary, ordinal, nonnegative continuous) found in scientific applications. This model class subsumes the traditional eigenmodel by embedding it in a general...
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作者：Dukes, O.; Richardson, D. B.; Shahn, Z.; Robins, J. M.; Tchetgen, E. J. Tchetgen
作者单位：Ghent University; University of California System; University of California Irvine; City University of New York (CUNY) System; Harvard University; Harvard T.H. Chan School of Public Health; University of Pennsylvania
摘要：Many proposals for the identification of causal effects require an instrumental variable that satisfies strong, untestable unconfoundedness and exclusion restriction assumptions. In this paper, we show how one can potentially identify causal effects under violations of these assumptions by harnessing a negative control population or outcome. This strategy allows one to leverage subpopulations for whom the exposure is degenerate, and requires that the instrument-outcome association satisfies a ...
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作者：Luo, Jiyu; Rava, Denise; Bradic, Jelena; Xu, Ronghui
作者单位：University of California System; University of California San Diego; University of California System; University of California San Diego
摘要：In this article we consider the marginal structural Cox model, which has been widely used to analyse observational studies with survival outcomes. The standard inverse probability weighting method under the model hinges on a propensity score model for the treatment assignment and a censoring model that incorporates both the treatment and the covariates. In such settings model misspecification can often occur, and the Cox regression model's non-collapsibility has historically posed challenges w...
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作者：Graham, E.; Carone, M.; Rotnitzky, A.
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作者：Cattaneo, Matias D.; Han, Fang; Lin, Zhexiao
作者单位：Princeton University; University of Washington; University of Washington Seattle; University of California System; University of California Berkeley
摘要：In two influential contributions, Rosenbaum (2005, 2020a) advocated for using the distances between componentwise ranks, instead of the original data values, to measure covariate similarity when constructing matching estimators of average treatment effects. While the intuitive benefits of using covariate ranks for matching estimation are apparent, there is no theoretical understanding of such procedures in the literature. We fill this gap by demonstrating that Rosenbaum's rank-based matching e...
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作者：Sang, Peijun; Kong, Dehan; Yang, Shu
作者单位：University of Waterloo; University of Toronto; North Carolina State University
摘要：Functional principal component analysis has been shown to be invaluable for revealing variation modes of longitudinal outcomes, which serve as important building blocks for forecasting and model building. Decades of research have advanced methods for functional principal component analysis, often assuming independence between the observation times and longitudinal outcomes. Yet such assumptions are fragile in real-world settings where observation times may be driven by outcome-related processe...
