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In this Issue

Table of Contents

  1. Assessing Treatment Effect Variation in Observational Studies: Results from a Data Challenge
  2. Carlos Carvalho, Avi Feller, Jared Murray, Spencer Woody, David Yeager
  3. pp. 21-35
  4. DOI: https://doi.org/10.1353/obs.2019.0000
  5. open access
  1. Estimating Treatment Effects with Causal Forests: An Application
  2. Susan Athey, Stefan Wager
  3. pp. 37-51
  4. DOI: https://doi.org/10.1353/obs.2019.0001
  5. open access
  1. Examining treatment effect heterogeneity using BART
  2. Nicole Carnegie, Vincent Dorie, Jennifer L. Hill
  3. pp. 52-70
  4. DOI: https://doi.org/10.1353/obs.2019.0002
  5. open access
  1. Machine Learning Analysis of Heterogeneity in the Effect of Student Mindset Interventions
  2. Fredrik D. Johansson
  3. pp. 71-82
  4. DOI: https://doi.org/10.1353/obs.2019.0003
  5. open access
  1. Matching with attention to effect modification in a data challenge
  2. Luke Keele, Samuel D. Pimentel
  3. pp. 83-92
  4. DOI: https://doi.org/10.1353/obs.2019.0004
  5. open access
  1. Heterogeneous Subgroup Identification with Observational Data: A Case Study Based on the National Study of Learning Mindsets
  2. Bryan Keller, Jianshen Chen, Tianyang Zhang
  3. pp. 93-104
  4. DOI: https://doi.org/10.1353/obs.2019.0010
  5. open access
  1. Causaltoolbox—Estimator Stability for Heterogeneous Treatment Effects
  2. Sören R. Künzel, Simon J. S. Walter, Jasjeet S. Sekhon
  3. pp. 105-117
  4. DOI: https://doi.org/10.1353/obs.2019.0005
  5. open access
  1. An Application of Matching After Learning To Stretch (MALTS) to the ACIC 2018 Causal Inference Challenge Data
  2. Harsh Parikh, Cynthia Rudin, Alexander Volfovsky
  3. pp. 118-130
  4. DOI: https://doi.org/10.1353/obs.2019.0006
  5. open access
  1. Selective Inference for Effect Modification: An Empirical Investigation
  2. Qingyuan Zhao, Snigdha Panigrahi
  3. pp. 131-140
  4. DOI: https://doi.org/10.1353/obs.2019.0007
  5. open access