Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Shahn, Zach"'
Autor:
Shahn, Zach, Hatfield, Laura
Consider a very general setting in which data on an outcome of interest is collected in two `groups' at two time periods, with certain group-periods deemed `treated' and others `untreated'. A special case is the canonical Difference-in-Differences (D
Externí odkaz:
http://arxiv.org/abs/2408.16039
Despite the common occurrence of interference in Difference-in-Differences (DiD) applications, standard DiD methods rely on an assumption that interference is absent, and comparatively little work has considered how to accommodate and learn about spi
Externí odkaz:
http://arxiv.org/abs/2405.11781
Methods for estimating heterogeneous treatment effects (HTE) from observational data have largely focused on continuous or binary outcomes, with less attention paid to survival outcomes and almost none to settings with competing risks. In this work,
Externí odkaz:
http://arxiv.org/abs/2401.11263
Autor:
Shahn, Zach
Suppose it is of interest to characterize effect heterogeneity of an intervention across levels of a baseline covariate using only pre- and post- intervention outcome measurements from those who received the intervention, i.e. with no control group.
Externí odkaz:
http://arxiv.org/abs/2306.11030
Autor:
Xu, Shenbo, Zheng, Bang, Su, Bowen, Finkelstein, Stan, Welsch, Roy, Ng, Kenney, Tzoulaki, Ioanna, Shahn, Zach
In empirical studies with time-to-event outcomes, investigators often leverage observational data to conduct causal inference on the effect of exposure when randomized controlled trial data is unavailable. Model misspecification and lack of overlap a
Externí odkaz:
http://arxiv.org/abs/2305.02373
Publikováno v:
Journal of Causal Inference, Vol 12, Iss 1, Pp 1175-91 (2024)
Causal inference from observational data often rests on the unverifiable assumption of no unmeasured confounding. Recently, Tchetgen Tchetgen and colleagues have introduced proximal inference to leverage negative control outcomes and exposures as pro
Externí odkaz:
https://doaj.org/article/d42ecff809d54726b3bf121501c02221
Causal inference from observational data often rests on the unverifiable assumption of no unmeasured confounding. Recently, Tchetgen Tchetgen and colleagues have introduced proximal inference to leverage negative control outcomes and exposures as pro
Externí odkaz:
http://arxiv.org/abs/2208.00105
Autor:
Shahn, Zach
Under what circumstances is it a threat to the parallel trends assumption required for Difference in Differences (DiD) studies if treatment decisions are based on past values of the outcome? We explore via simulation studies whether parallel trends h
Externí odkaz:
http://arxiv.org/abs/2207.13178
In this paper, we generalize methods in the Difference in Differences (DiD) literature by showing that both additive and multiplicative standard and coarse Structural Nested Mean Models (Robins, 1994, 1997, 1998, 2000, 2004; Lok and Degruttola, 2012;
Externí odkaz:
http://arxiv.org/abs/2204.10291
Many proposals for the identification of causal effects in the presence of unmeasured confounding require an instrumental variable or negative control that satisfies strong, untestable exclusion restrictions. In this paper, we will instead show how o
Externí odkaz:
http://arxiv.org/abs/2204.04119