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Efficient adjustment sets for population average treatment effect
  estimation in non-parametric causal graphical models

Efficient adjustment sets for population average treatment effect estimation in non-parametric causal graphical models

1 December 2019
A. Rotnitzky
Ezequiel Smucler
    CML
ArXivPDFHTML

Papers citing "Efficient adjustment sets for population average treatment effect estimation in non-parametric causal graphical models"

4 / 4 papers shown
Title
Efficient Online Estimation of Causal Effects by Deciding What to
  Observe
Efficient Online Estimation of Causal Effects by Deciding What to Observe
Shantanu Gupta
Zachary Chase Lipton
David Benjamin Childers
CML
32
18
0
20 Aug 2021
Necessary and sufficient graphical conditions for optimal adjustment
  sets in causal graphical models with hidden variables
Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables
Jakob Runge
CML
4
25
0
20 Feb 2021
Semiparametric Inference For Causal Effects In Graphical Models With
  Hidden Variables
Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables
Rohit Bhattacharya
Razieh Nabi
I. Shpitser
CML
26
64
0
27 Mar 2020
Causal Inference and Causal Explanation with Background Knowledge
Causal Inference and Causal Explanation with Background Knowledge
Christopher Meek
CML
224
628
0
20 Feb 2013
1