Estimating the causal effects of marketing interventions using propensity score methodology

DOI10.1214/088342306000000259zbMath1426.62325arXivmath/0609201OpenAlexW3098795573MaRDI QIDQ2381782

Donald B. Rubin, Richard P. Waterman

Publication date: 18 September 2007

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

Full work available at URL: https://arxiv.org/abs/math/0609201

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (6)

The Central Role of Bayes’ Theorem for Joint Estimation of Causal Effects and Propensity Scores ⋮ Full matching approach to instrumental variables estimation with application to the effect of malaria on stunting ⋮ Do CRM systems cause one-to-one marketing effectiveness? ⋮ For objective causal inference, design trumps analysis ⋮ Inferring causal impact using Bayesian structural time-series models ⋮ Assessing the causal effect of binary interventions from observational panel data with few treated units

Cites Work

• Comment on J. Neyman and causal inference in experiments and observational studies: On the application of probability theory to agricultural experiments. Essay on principles. Section 9 [Ann. Agric. Sci. 10 (1923), 1--51]
• The central role of the propensity score in observational studies for causal effects
• Statistics and Causal Inference
• The role of the propensity score in estimating dose-response functions
• Substantial Gains in Bias Reduction from Matching with a Variable Number of Controls
• Causal Inference With General Treatment Regimes
• Causal Inference Using Potential Outcomes
• Unnamed Item

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