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Table of Contents

  1. Editor's Note
  2. Nandita Mitra
  3. pp. 1-2
  4. DOI: https://doi.org/10.1353/obs.2023.0022
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  1. Causal Methods Madness: Lessons Learned from the 2022 ACIC Competition to Estimate Health Policy Impacts
  2. Dan R.C. Thal, Mariel M. Finucane
  3. pp. 3-27
  4. DOI: https://doi.org/10.1353/obs.2023.0023
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  1. Bayesian Causal Forests & the 2022 ACIC Data Challenge: Scalability and Sensitivity
  2. Ajinkya H. Kokandakar, Hyunseung Kang, Sameer K. Deshpande
  3. pp. 29-41
  4. DOI: https://doi.org/10.1353/obs.2023.0024
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  1. Doubly Robust Estimation of Average Treatment Effects on the Treated through Marginal Structural Models
  2. Michael Schomaker, Philipp F. M. Baumann
  3. pp. 43-57
  4. DOI: https://doi.org/10.1353/obs.2023.0025
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  1. Estimating Treatment Effects over Time with Causal Forests: An application to the ACIC 2022 Data Challenge
  2. Shu Wan, Guanghui Zhang
  3. pp. 59-71
  4. DOI: https://doi.org/10.1353/obs.2023.0026
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  1. Inverse Probability Weighting Difference-in-Differences (IPWDID)
  2. Yuqin Wei, Matthew Epland, Jingyuan (Hannah) Liu
  3. pp. 73-81
  4. DOI: https://doi.org/10.1353/obs.2023.0027
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  1. Estimating Treatment Effect with Propensity Score Weighted Regression and Double Machine Learning
  2. Jun Xue, Wei Zhong Goh, Dana Rotz
  3. pp. 83-90
  4. DOI: https://doi.org/10.1353/obs.2023.0028
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