Statistical Science

Comment: Contributions of Model Features to BART Causal Inference Performance Using ACIC 2016 Competition Data

Nicole Bohme Carnegie

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Abstract

With a thorough exposition of the methods and results of the 2016 Atlantic Causal Inference Competition, Dorie et al. have set a new standard for reproducibility and comparability of evaluations of causal inference methods. In particular, the open-source R package aciccomp2016, which permits reproduction of all datasets used in the competition, will be an invaluable resource for evaluation of future methodological developments.

Building upon results from Dorie et al., we examine whether a set of potential modifications to Bayesian Additive Regression Trees (BART)—multiple chains in model fitting, using the propensity score as a covariate, targeted maximum likelihood estimation (TMLE), and computing symmetric confidence intervals—have a stronger impact on bias, RMSE, and confidence interval coverage in combination than they do alone. We find that bias in the estimate of SATT is minimal, regardless of the BART formulation. For purposes of CI coverage, however, all proposed modifications are beneficial—alone and in combination—but use of TMLE is least beneficial for coverage and results in considerably wider confidence intervals.

Article information

Source
Statist. Sci., Volume 34, Number 1 (2019), 90-93.

Dates
First available in Project Euclid: 12 April 2019

Permanent link to this document
https://projecteuclid.org/euclid.ss/1555056036

Digital Object Identifier
doi:10.1214/18-STS682

Mathematical Reviews number (MathSciNet)
MR3938969

Zentralblatt MATH identifier
07110680

Keywords
Bayesian additive regression trees TMLE propensity score

Citation

Carnegie, Nicole Bohme. Comment: Contributions of Model Features to BART Causal Inference Performance Using ACIC 2016 Competition Data. Statist. Sci. 34 (2019), no. 1, 90--93. doi:10.1214/18-STS682. https://projecteuclid.org/euclid.ss/1555056036


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References

  • [1] Chipman, H. A., George, E. I. and McCulloch, R. E. (2010). BART: Bayesian additive regression trees. Ann. Appl. Stat. 4 266–298.
  • [2] Dorie, V. (2017). aciccomp2016: Atlantic Causal Inference Conference Competition 2016 Simulation. R package version 0.1-0.
  • [3] Dorie, V., Hill, J., Shalit, U., Scott, M. and Cervone, D. (2019). Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition. Statist. Sci. 34 43–68.
  • [4] Hahn, P. R., Murray, J. S. and Carvalho, C. M. (2017). Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects. Preprint. Available at arXiv:1706.09523 [stat.ME].
  • [5] R Core Team (2018). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
  • [6] van der Laan, M. J. and Rubin, D. (2006). Targeted maximum likelihood learning. Int. J. Biostat. 2 Art. 11, 40.

See also

  • Main article: Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition.