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doubleml-for-py

CLAUDE.md795 starsChanged 4 months agoBSD-3-ClauseView on GitHub

DoubleML is a Python package implementing Double/Debiased Machine Learning (DML) methods for causal inference: - Partially Linear Models (PLR, PLIV, PLPR, LPLR) - Interactive Regression Models (IRM, IIVM, APO, QTE, CVAR, SSM) - Difference-in-Differences estimators (DID, DIDCSBinary, DIDMulti) - Regression Discontinuity Design (RDD) Docs: https://docs.doubleml.org | Source: https://github.com/DoubleML/doubleml-for-py Branch status & TODOs: .claude/STATUS.md Core: numpy>=2.0.0, pandas>=2.0.0, scipy>=1.7.0, scikit-learn>=1.6.0, statsmodels>=0.14.0 ML/Tuning: optuna>=4.6.0, joblib>=1.2.0 Visualization: matplotlib>=3.9.0, seaborn>=0.13, plotly>=5.0.0 Dev: pytest>=8.3.0, black>=25.1.0, ruff>=0.11.1, mypy>=1.18.0, xgboost>=2.1.0, lightgbm>=4.6.0 Before completing any task: Detailed conventions are…

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CLAUDE.md

DoubleML for Python

DoubleML is a Python package implementing Double/Debiased Machine Learning (DML) methods for causal inference:

  • Partially Linear Models (PLR, PLIV, PLPR, LPLR)
  • Interactive Regression Models (IRM, IIVM, APO, QTE, CVAR, SSM)
  • Difference-in-Differences estimators (DID, DIDCSBinary, DIDMulti)
  • Regression Discontinuity Design (RDD)

Docs: https://docs.doubleml.org | Source: https://github.com/DoubleML/doubleml-for-py

Branch status & TODOs: .claude/STATUS.md

Architecture

Class Hierarchy

DoubleMLBase (ABC)
└─> DoubleMLScalar (ABC) - single-parameter models
    ├─> LinearScoreMixin - closed-form solver (θ = -E[ψ_b]/E[ψ_a])
    │   ├─> DoubleMLPLR
    │   ├─> DoubleMLIRM
    │   ├─> DoubleMLPLIV
    │   ├─> DoubleMLIIVM
    │   └─> DoubleML DID variants
    └─> NonLinearScoreMixin - numerical solver (planned)

DoubleML - multi-parameter estimation (extends DoubleMLScalar)

Design Patterns

  • Template Method: fit() orchestrates; subclasses implement _nuisance_est(), _get_score_elements()
  • Mixin Pattern: LinearScoreMixin provides closed-form coefficient estimation
  • Delegation: DoubleMLBase delegates inference to DoubleMLFramework

Core Files

FilePurpose
doubleml/double_ml_base.pyAbstract base with properties (coef, se, summary) and inference
doubleml/double_ml_scalar.pySingle-parameter estimation orchestrator
doubleml/double_ml.pyMulti-parameter estimation with sample splitting
doubleml/double_ml_framework.pyStatistical inference (confint, bootstrap, sensitivity)
doubleml/double_ml_linear_score.pyLinear score mixin

Package Structure

doubleml/
├── data/          # Data containers (DoubleMLData, DoubleMLDIDData, etc.)
├── plm/           # Partially Linear Models (PLR, PLIV, PLPR, LPLR)
├── irm/           # Interactive Regression Models (IRM, IIVM, APO, QTE, etc.)
├── did/           # Difference-in-Differences estimators
├── rdd/           # Regression Discontinuity Design
├── utils/         # Helpers (_checks, _estimation, resampling, tuning)
└── tests/         # Main test directory

Key Dependencies

Core: numpy>=2.0.0, pandas>=2.0.0, scipy>=1.7.0, scikit-learn>=1.6.0, statsmodels>=0.14.0 ML/Tuning: optuna>=4.6.0, joblib>=1.2.0 Visualization: matplotlib>=3.9.0, seaborn>=0.13, plotly>=5.0.0 Dev: pytest>=8.3.0, black>=25.1.0, ruff>=0.11.1, mypy>=1.18.0, xgboost>=2.1.0, lightgbm>=4.6.0

Git Workflow

  • Main branch: main
  • Commits: Conventional Commits — feat:, fix:, docs:, refactor:, test:, chore:

Verification

Before completing any task:

black .                    # Format
ruff check --fix .         # Lint
mypy                       # Type check (allowlist: [tool.mypy] files in pyproject.toml)
pytest -m ci               # Tests

Coding Standards

Detailed conventions are in .claude/rules/:

  • py-code-conventions.md — Formatting, type hints, docstrings, naming, DML-specific patterns
  • error-handling.md — Exception types, validation patterns, warnings vs. errors
  • performance-guidelines.md — Vectorization, pre-allocation, DML computation patterns
  • testing-conventions.md — Markers, fixtures, assertion patterns
  • dml-scalar-test-structure.md — Mandatory 5-file test structure for scalar models

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