cff-version: 1.2.0
message: "If you use StatsPAI in your research, please cite it using the metadata below."
title: "StatsPAI: Validation-Tiered Causal Inference and Econometrics Workflows for Python"
abstract: >-
  StatsPAI is a validation-tiered Python toolkit for applied econometrics and
  causal inference covering difference-in-differences, instrumental variables,
  regression discontinuity, synthetic control, double machine learning,
  meta-learners, causal forests, causal discovery, structural estimation,
  panel data, spatial models, and time-series analysis. Public estimators return
  structured result objects with common summary, plotting, export, citation,
  audit, and serialization methods where supported. The registry records
  stability and validation status separately, so API breadth is not presented as
  numerical validation evidence, and the callable surface is exposed through a
  single ``import statspai as sp`` entry point plus machine-readable schemas.
type: software
authors:
  - family-names: "Wang"
    given-names: "Biaoyue"
    affiliation: "Stanford REAP, Stanford University"
    email: "brycew6m@stanford.edu"
  - family-names: "Rozelle"
    given-names: "Scott"
    affiliation: "Stanford REAP, Stanford University"
    email: "rozelle@stanford.edu"
version: "1.16.1"
date-released: "2026-06-01"
license: MIT
# Zenodo concept DOI; always resolves to the latest archived version.
# For a specific-version DOI (e.g. for replication packages), follow the
# concept DOI link below and pick the matching release in the version
# history sidebar on Zenodo.
doi: "10.5281/zenodo.19933900"
url: "https://github.com/brycewang-stanford/StatsPAI"
repository-code: "https://github.com/brycewang-stanford/StatsPAI"
repository-artifact: "https://pypi.org/project/StatsPAI/"
identifiers:
  - type: doi
    value: "10.5281/zenodo.19933900"
    description: "Zenodo concept DOI; always resolves to the latest version."
keywords:
  - econometrics
  - causal-inference
  - difference-in-differences
  - regression-discontinuity
  - synthetic-control
  - instrumental-variables
  - double-machine-learning
  - meta-learners
  - panel-data
  - bayesian-causal-forest
  - causal-discovery
  - tmle
  - python
  - agent-facing
# When the JOSS paper is accepted, fill in DOI / volume / pages and uncomment.
# Until then, cite the software entry above (versioned Zenodo DOI is
# preferable once the GitHub-Zenodo integration mints one for a release).
#
# preferred-citation:
#   type: article
#   authors:
#     - family-names: "Wang"
#       given-names: "Biaoyue"
#       affiliation: "Stanford REAP, Stanford University"
#       email: "brycew6m@stanford.edu"
#     - family-names: "Rozelle"
#       given-names: "Scott"
#       affiliation: "Stanford REAP, Stanford University"
#       email: "rozelle@stanford.edu"
#   title: "StatsPAI: A Unified, Agent-Native Python Toolkit for Causal Inference and Applied Econometrics"
#   journal: "Journal of Open Source Software"
#   year: 2026
#   doi: "TBD"
