Search Results for author: Elizabeth L. Ogburn

Found 9 papers, 4 papers with code

Augmented balancing weights as linear regression

no code implementations27 Apr 2023 David Bruns-Smith, Oliver Dukes, Avi Feller, Elizabeth L. Ogburn

These popular doubly robust or double machine learning estimators combine outcome modeling with balancing weights -- weights that achieve covariate balance directly in lieu of estimating and inverting the propensity score.


Causal mediation analysis: From simple to more robust strategies for estimation of marginal natural (in)direct effects

1 code implementation11 Feb 2021 Trang Quynh Nguyen, Elizabeth B. Sarker, Ian Schmid, Noah Greifer, Elizabeth L. Ogburn, Ina M. Koning, Elizabeth A. Stuart

A second goal is to provide a "menu" of estimators that practitioners can choose from for the estimation of marginal natural (in)direct effects.

Methodology 62D20

Counterexamples to "The Blessings of Multiple Causes" by Wang and Blei

no code implementations17 Jan 2020 Elizabeth L. Ogburn, Ilya Shpitser, Eric J. Tchetgen Tchetgen

This note has been updated (April, 2020) to respond to "Towards Clarifying the Theory of the Deconfounder" by Yixin Wang, David M. Blei (arXiv:2003. 04948).

Comment on "Blessings of Multiple Causes"

no code implementations11 Oct 2019 Elizabeth L. Ogburn, Ilya Shpitser, Eric J. Tchetgen Tchetgen

(This comment has been updated to respond to Wang and Blei's rejoinder [arXiv:1910. 07320].)

Causal Inference valid

Network Dependence Can Lead to Spurious Associations and Invalid Inference

1 code implementation30 Jul 2019 Youjin Lee, Elizabeth L. Ogburn

Researchers across the health and social sciences generally assume that observations are independent, even while relying on convenience samples that draw subjects from one or a small number of communities, schools, hospitals, etc.


Causal inference, social networks, and chain graphs

3 code implementations12 Dec 2018 Elizabeth L. Ogburn, Ilya Shpitser, Youjin Lee

Traditionally, statistical and causal inference on human subjects relies on the assumption that individuals are independently affected by treatments or exposures.


Causal inference for social network data

1 code implementation23 May 2017 Elizabeth L. Ogburn, Oleg Sofrygin, Ivan Diaz, Mark J. Van Der Laan

We describe semiparametric estimation and inference for causal effects using observational data from a single social network.

Methodology Statistics Theory Statistics Theory

Causal Diagrams for Interference

no code implementations5 Mar 2014 Elizabeth L. Ogburn, Tyler J. VanderWeele

The first causal mechanism by which interference can operate is a direct causal effect of one individual's treatment on another individual's outcome; we call this direct interference.


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