Doubly Robust Causal Effect Estimation under Networked ...

Double Robust Efficient Estimators of Longitudinal Treatment Effects

Comprehensive R Archive Network (CRAN). ... New York: Wiley,. 2005;4:2619–25. [7]. Bang H, Robins JM. Doubly robust estimation in missing data and causal ...

Doubly Robust Estimation of Causal Effects in R - YouTube

Doubly Robust Estimation of Causal Effects in R by Dr. Sebastian Teran Hidalgo. Visit https://rstats.ai/nyr/ to learn more.

Learning causality with graphs - AAAI Publications

“Estimating average causal effects under general interference, with application to a social network ... “Doubly robust estimation of causal.

Is double machine learning doubly robust? If so, how?

2016 relies on a doubly robust estimator (e.g. in the context for the average treatment effect it uses augmented inverse probability weights).

Understanding AIPW, the Doubly-Robust Estimator

When estimating causal effects, the gold standard is randomized controlled trials or AB tests. By randomly exposing units to a treatment, we ...

Doubly Robust Causal Effect Estimation under Networked ...

Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning. Corresponding author: [email protected]. First author: chenweilin ...

Doubly Robust Estimation of Local Average Treatment Effects Using ...

Unlike previous approaches, our doubly robust (DR) estimation procedures use quasi-likelihood methods weighted by the inverse of the IV propensity score - so- ...

Identification and estimation of causal peer effects using double ...

In the literature of causal peer effects, Egami (2018) exploits an NCO, and Liu and. Tchetgen Tchetgen (2020) use an NCE to address unmeasured network ...

Bias-Reduced Doubly Robust Estimation - Taylor & Francis Online

Over the past decade, doubly robust estimators have been proposed for a variety of target parameters in causal inference and missing data models ...

DAG-aware Transformer for Causal Effect Estimation - OpenReview

Doubly robust estimation in missing data and causal inference models. ... Causal inference under networked interference and intervention policy enhancement.

Doubly Robust Identification for Causal Panel Data Models

We study identification and estimation of causal effects in settings with panel data. Traditionally, researchers follow model-based identification ...

An improved multiply robust estimator for the average treatment effect

In observational studies, double robust or multiply robust (MR) approaches provide more protection from model misspecification than the ...

Bias-Reduced Doubly Robust Estimation - jstor

Over the past decade, doubly robust estimators have been proposed for a variety of target parameters in causal inference and missing data models.

Ruoxuan Xiong: Efficient Treatment Effect Estimation ... - YouTube

Ruoxuan Xiong: Efficient Treatment Effect Estimation under Heterogeneous Partial Interference · Online Causal Inference Seminar · Oliver Dukes: ...

Implementing Double-robust Estimators of Causal Effects

A double-robust estimator gives the analyst two opportunities for obtaining unbiased inference when adjusting for selection effects such as confounding by ...

Doubly Robust Estimation — Causal Inference for the Brave and True

Doubly Robust Estimation is a way of combining propensity score and linear regression in a way you don't have to rely on either of them.

Connections Between Multicalibration and Doubly Robust Estimation

Doubly-Robust Estimation for Correcting Position-Bias in Clicks for ... Double Machine Learning for Causal and Treatment Effects. Becker ...

Doubly robust confidence sequences for sequential causal inference

Ian Waudby-Smith is a PhD student in Statistics at Carnegie Mellon University, advised by Aaditya Ramdas.

Journal of Machine Learning Research

On Doubly Robust Inference for Double Machine Learning in Semiparametric Regression ... DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal ...

Doubly Robust Learning — econml 0.15.1 documentation

In this library we implement recent modifications to the doubly robust approach that allow for the estimation of heterogeneous treatment effects (see e.g. [ ...