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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "flexBCF" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'flexBCF: Fast & Flexible Implementation of Bayesian Causal Forests'
version: 1.0.3
identifiers:
- type: doi
  value: 10.32614/CRAN.package.flexBCF
abstract: A faster implementation of Bayesian Causal Forests (BCF; Hahn et al. (2020)
  <https://doi.org/10.1214/19-BA1195>), which uses regression tree ensembles to estimate
  the conditional average treatment effect of a binary treatment on a scalar output
  as a function of many covariates. This implementation avoids many redundant computations
  and memory allocations present in the original BCF implementation, allowing the
  model to be fit to larger datasets. The implementation was originally developed
  for the 2022 American Causal Inference Conference's Data Challenge. See Kokandakar
  et al. (2023) <https://doi.org/10.1353/obs.2023.0024> for more details.
authors:
- family-names: Deshpande
  given-names: Sameer K.
  email: sameer.deshpande@wisc.edu
  orcid: https://orcid.org/0000-0003-4116-5533
- family-names: Kokandakar
  given-names: Ajinkya H.
  email: ajinkyakokandakar@gmail.com
  orcid: https://orcid.org/0000-0001-6628-2272
preferred-citation:
  type: manual
  title: flexBCF
  authors:
  - family-names: Deshpande
    given-names: Sameer K
  - family-names: Kokandakar
    given-names: Ajinkya H
  year: '2025'
  notes: R package version 1.0.3
repository: https://skdeshpande91.r-universe.dev
repository-code: https://github.com/skdeshpande91/flexBCF
commit: cf6e51f8c1e0c7a3c1f7bfa837196f27bbee751e
url: https://github.com/skdeshpande91/flexBCF
date-released: '2025-12-01'
contact:
- family-names: Deshpande
  given-names: Sameer K.
  email: sameer.deshpande@wisc.edu
  orcid: https://orcid.org/0000-0003-4116-5533