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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：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...
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作者：Gao, Chenyin; Yang, Shu; Kim, Jae Kwang
作者单位：North Carolina State University; Iowa State University
摘要：Calibration weighting has been widely used to correct selection biases in nonprobability sampling, missing data and causal inference. The main idea is to calibrate the biased sample to the benchmark by adjusting the subject weights. However, hard calibration can produce enormous weights when an exact calibration is enforced on a large set of extraneous covariates. This article proposes a soft calibration scheme, where the outcome and the selection indicator follow mixed-effect models. The sche...
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作者：Li, Sijia; Luedtke, Alex
作者单位：University of Washington; University of Washington Seattle
摘要：We aim to make inferences about a smooth, finite-dimensional parameter by fusing together data from multiple sources. Previous works have studied the estimation of a variety of parameters in similar data fusion settings, including estimation of the average treatment effect and average reward under a policy, with the majority of them merging one historical data source with covariates, actions and rewards, and one data source of the same covariates. In this article, we consider the general case ...
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作者：Kennedy, E. H.; Balakrishnan, S.; Wasserman, L. A.
作者单位：Carnegie Mellon University
摘要：Causal effects are often characterized with averages, which can give an incomplete picture of the underlying counterfactual distributions. Here we consider estimating the entire counterfactual density and generic functionals thereof. We focus on two kinds of target parameters: density approximations and the distance between counterfactual densities. We study nonparametric efficiency bounds, giving results for smooth but otherwise generic models and distances. Importantly, we show how these bou...
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作者：Dukes, Oliver; Shpitser, Ilya; Tchetgen, Eric J. Tchetgen
作者单位：Ghent University; Johns Hopkins University; University of Pennsylvania
摘要：A common concern when trying to draw causal inferences from observational data is that the measured covariates are insufficiently rich to account for all sources of confounding. In practice, many of the covariates may only be proxies of the latent confounding mechanism. Recent work has shown that in certain settings where the standard no-unmeasured-confounding assumption fails, proxy variables can be leveraged to identify causal effects. Results currently exist for the total causal effect of a...
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作者：Ning, Yang; Duan, Jingyi
作者单位：Cornell University
