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How and Why to Use Experimental Data to Evaluate Methods for
  Observational Causal Inference

How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference

6 October 2020
A. Gentzel
Purva Pruthi
David D. Jensen
    CML
ArXivPDFHTML

Papers citing "How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference"

5 / 5 papers shown
Title
Compositional Models for Estimating Causal Effects
Compositional Models for Estimating Causal Effects
Purva Pruthi
David D. Jensen
CML
67
0
0
25 Jun 2024
Stochastic Causal Programming for Bounding Treatment Effects
Stochastic Causal Programming for Bounding Treatment Effects
Kirtan Padh
Jakob Zeitler
David S. Watson
Matt J. Kusner
Ricardo M. A. Silva
Niki Kilbertus
CML
32
26
0
22 Feb 2022
Validating Causal Inference Methods
Validating Causal Inference Methods
Harsh Parikh
Carlos Varjao
Louise Xu
E. T. Tchetgen
CML
19
20
0
09 Feb 2022
Evaluation Methods and Measures for Causal Learning Algorithms
Evaluation Methods and Measures for Causal Learning Algorithms
Lu Cheng
Ruocheng Guo
Raha Moraffah
Paras Sheth
K. S. Candan
Huan Liu
CML
ELM
24
51
0
07 Feb 2022
ADCB: An Alzheimer's disease benchmark for evaluating observational
  estimators of causal effects
ADCB: An Alzheimer's disease benchmark for evaluating observational estimators of causal effects
N. M. Kinyanjui
Fredrik D. Johansson
CML
30
0
0
12 Nov 2021
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