Comment: strengthening empirical evaluation of causal inference methods

DOI10.1214/18-STS690zbMath1420.62349OpenAlexW2937018884WikidataQ128057551 ScholiaQ128057551MaRDI QIDQ2325612

David D. Jensen

Publication date: 27 September 2019

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ss/1555056033

zbMATH Keywords

alignmentalgorithmic complexityempirical evaluationmachine learningcausal inferenceconstructed observational studies

Mathematics Subject Classification ID

Theory of statistical experiments (62B15) Learning and adaptive systems in artificial intelligence (68T05) Response surface designs (62K20)

Related Items (1)

Estimating causal effects with optimization-based methods: a review and empirical comparison

Uses Software

• TETRAD
• GeneNetWeaver

Cites Work

• Causation, prediction, and search
• On the optimality of the simple Bayesian classifier under zero-one loss
• Automated versus do-it-yourself methods for causal inference: lessons learned from a data analysis competition
• Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments
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