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pro vyhledávání: '"Tchetgen, Eric J. Tchetgen"'
Unmeasured confounding is one of the major concerns in causal inference from observational data. Proximal causal inference (PCI) is an emerging methodological framework to detect and potentially account for confounding bias by carefully leveraging a
Externí odkaz:
http://arxiv.org/abs/2409.08924
Negative controls are increasingly used to evaluate the presence of potential unmeasured confounding in observational studies. Beyond the use of negative controls to detect the presence of residual confounding, proximal causal inference (PCI) was rec
Externí odkaz:
http://arxiv.org/abs/2402.00335
The synthetic control method (SCM) has become a popular tool for estimating causal effects in policy evaluation, where a single treated unit is observed, and a heterogeneous set of untreated units with pre- and post-policy change data are also observ
Externí odkaz:
http://arxiv.org/abs/2308.09527
Autor:
Gkatzionis, Apostolos, Tchetgen, Eric J. Tchetgen, Heron, Jon, Northstone, Kate, Tilling, Kate
Selection bias is a common concern in epidemiologic studies. In the literature, selection bias is often viewed as a missing data problem. Popular approaches to adjust for bias due to missing data, such as inverse probability weighting, rely on the as
Externí odkaz:
http://arxiv.org/abs/2208.02657
We introduce a self-censoring model for multivariate nonignorable nonmonotone missing data, where the missingness process of each outcome is affected by its own value and is associated with missingness indicators of other outcomes, while conditionall
Externí odkaz:
http://arxiv.org/abs/2207.08535
Contrasting marginal counterfactual survival curves across treatment arms is an effective and popular approach for inferring the causal effect of an intervention on a right-censored time-to-event outcome. A key challenge to drawing such inferences in
Externí odkaz:
http://arxiv.org/abs/2204.13144
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 co
Externí odkaz:
http://arxiv.org/abs/2109.11904
A standard assumption for causal inference about the joint effects of time-varying treatment is that one has measured sufficient covariates to ensure that within covariate strata, subjects are exchangeable across observed treatment values, also known
Externí odkaz:
http://arxiv.org/abs/2109.07030
Scientists have been interested in estimating causal peer effects to understand how people's behaviors are affected by their network peers. However, it is well known that identification and estimation of causal peer effects are challenging in observa
Externí odkaz:
http://arxiv.org/abs/2109.01933
Unobserved confounding is a fundamental obstacle to establishing valid causal conclusions from observational data. Two complementary types of approaches have been developed to address this obstacle: obtaining identification using fortuitous external
Externí odkaz:
http://arxiv.org/abs/2108.06818