Search Results for author:
Found 9 papers, 4 papers with code
Augmented balancing weights as linear regression
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.
Prospective Learning: Principled Extrapolation to the Future
We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning.
Causal mediation analysis: From simple to more robust strategies for estimation of marginal natural (in)direct effects
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
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"
(This comment has been updated to respond to Wang and Blei's rejoinder [arXiv:1910. 07320].)
Network Dependence Can Lead to Spurious Associations and Invalid Inference
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.
Applications
Causal inference, social networks, and chain graphs
Traditionally, statistical and causal inference on human subjects relies on the assumption that individuals are independently affected by treatments or exposures.
Methodology
Causal inference for social network data
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
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.
Methodology
