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作者：Shi, J.; Wu, Z.; Dempsey, W.
作者单位：University of Michigan System; University of Michigan
摘要：The micro-randomized trial is a sequential randomized experimental design to empirically evaluate the effectiveness of mobile health intervention components that may be delivered at hundreds or thousands of decision points. Micro-randomized trials have motivated a new class of causal estimands, termed causal excursion effects, for which semiparametric inference can be conducted via a weighted, centred least-squares criterion (Boruvka et al., 2018). Causal excursion effects allow health scienti...
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作者：Klaassen, S.; Kueck, J.; Spindler, M.; Chernozhukov, V
作者单位：University of Hamburg; Massachusetts Institute of Technology (MIT)
摘要：Graphical models have become a popular tool for representing dependencies within large sets of variables and are crucial for representing causal structures. We provide results for uniform inference on high-dimensional graphical models, in which the number of target parameters d is potentially much larger than the sample size, under approximate sparsity. Our results highlight how graphical models can be estimated and recovered using modern machine learning methods in high-dimensional complex se...
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作者：Cui, Y.; Michael, H.; Tanser, F.; Tchetgen, E. Tchetgen
作者单位：National University of Singapore; University of Massachusetts System; University of Massachusetts Amherst; University of Lincoln; University of Pennsylvania
摘要：Robins (1998) introduced marginal structural models, a general class of counterfactual models for the joint effects of time-varying treatments in complex longitudinal studies subject to time-varying confounding. Robins (1998) established the identification of marginal structural model parameters under a sequential randomization assumption, which rules out unmeasured confounding of treatment assignment over time. The marginal structural Cox model is one of the most popular marginal structural m...
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作者：Chernozhukov, V; Newey, W. K.; Singh, R.
作者单位：Massachusetts Institute of Technology (MIT)
摘要：Debiased machine learning is a meta-algorithm based on bias correction and sample splitting to calculate confidence intervals for functionals, i.e., scalar summaries, of machine learning algorithms. For example, an analyst may seek the confidence interval for a treatment effect estimated with a neural network. We present a non-asymptotic debiased machine learning theorem that encompasses any global or local functional of any machine learning algorithm that satisfies a few simple, interpretable...
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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：Chang, Hyunwoong; Cai, James J.; Zhou, Quan
作者单位：Texas A&M University System; Texas A&M University College Station; Texas A&M University System; Texas A&M University College Station
摘要：We propose an empirical Bayes formulation of the structure learning problem, where the prior specification assumes that all node variables have the same error variance, an assumption known to ensure the identifiability of the underlying causal directed acyclic graph. To facilitate efficient posterior computation, we approximate the posterior probability of each ordering by that of a best directed acyclic graph model, which naturally leads to an order-based Markov chain Monte Carlo algorithm. S...
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作者：Ning, Yang; Duan, Jingyi
作者单位：Cornell University
