Hi all,
Our next virtual meeting will be Wednesday, October 21, where we will hear
Eric Tchetgen Tchetgen presents research on "An Introduction to Proximal
Causal Learning".
*Abstract*: A standard assumption for causal inference from observational
data is that one has measured a sufficiently rich set of covariates to
ensure that within covariates strata, subjects are exchangeable across
observed treatment values. Skepticism about the exchangeability assumption
in observational studies is often warranted because it hinges on one's
ability to accurately measure covariates capturing all potential sources of
confounding. Realistically, confounding mechanisms can rarely if ever, be
learned with certainty from measured covariates. One can therefore only
ever hope that covariate measurements are at best proxies of true
underlying confounding mechanisms operating in an observational study, thus
invalidating causal claims made on basis of standard exchangeability
conditions. Causal learning from proxies is a challenging inverse problem
which has to date remained unresolved. In this paper, we introduce a formal
potential outcome framework for proximal causal learning, which while
explicitly acknowledging covariate measurements as imperfect proxies of
confounding mechanisms, offers an opportunity to learn about causal effects
in settings where exchangeability on the basis of measured covariates
fails. Sufficient conditions for nonparametric identification are given,
leading to the proximal g-formula and corresponding proximal g-computation
algorithm for estimation, both generalizations of Robins' foundational
g-formula and g-computation algorithm, which account explicitly for bias
due to unmeasured confounding. Both point treatment and time-varying
treatment settings are considered, and an application of proximal
g-computation of causal effects is given for illustration.
*Link to paper*:
https://arxiv.org/abs/2009.10982
*Zoom link*:
https://harvard.zoom.us/j/99424949004?pwd=aWtPNFM3ZzFYbWxIMXNoZDlyUElVZz09
*Schedule of the workshop*:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Best,
Soichiro
--
Soichiro Yamauchi
PhD candidate
Harvard University
URL:
https://soichiroy.github.io/