fd-cate 0.1.1

Front-door CATE/ATE estimation toolkit with debiased learners.

FD-CATE

Front-door CATE/ATE estimation toolkit with paper-parity defaults for debiased front-door learners.

This repository keeps the original research scripts (FDCATE.py, analyze_fars_2000_fd.py) and adds a standard-library interface (fd_cate) with a stable artifact contract.

Install

python -m pip install -U pip
python -m pip install fd-cate

Default learner is xgb (XGBoost). nn is also supported via nuisance_learner="nn".

One-Click Quickstart

fdcate demo --outdir ./fdcate-demo

This single command runs:

• synthetic data generation
• model fit + artifact contract write
• optional quick benchmark (enabled by default)

Expected files:

• ./fdcate-demo/synthetic.csv
• ./fdcate-demo/fit_out/summary.txt
• ./fdcate-demo/fit_out/results.json
• ./fdcate-demo/fit_out/diagnostics.json
• ./fdcate-demo/fit_out/effects.csv
• ./fdcate-demo/fit_out/model.pkl
• ./fdcate-demo/benchmark_quick.json (unless --run-benchmark false)

Quickstart (Python API)

from fd_cate import FDCATE
from FDCATE import simulate_fd_data_md

# synthetic example
D = simulate_fd_data_md(n=500, d=10, seed=0)

est = FDCATE(method="fd-dr", nuisance_learner="xgb", random_state=0)
est.fit(D.C, D.Y, t=D.X, m=D.Z)

tau = est.effect(D.C)
print(est.ate_)
print(est.summary())

Quickstart (CLI)

# generate synthetic csv
fdcate synthetic --n 300 --d 8 --seed 42 --out synthetic.csv

# fit + write standard artifacts
fdcate fit \
  --data synthetic.csv \
  --outcome y --treat t --med m \
  --outdir out/

# diagnostics only
fdcate doctor \
  --data synthetic.csv \
  --outcome y --treat t --med m

Standard artifacts under out/:

• summary.txt
• results.json
• diagnostics.json
• effects.csv
• model.pkl

Benchmark (Quick Profile + Golden Regression)

fd-cate now includes a deterministic quick benchmark profile for regression checks.

fdcate benchmark --n 120 --d 6 --seed 2026 --nuisance-learner xgb --out results/benchmark_quick.json

Multi-seed profile (recommended for robust comparisons):

fdcate benchmark \
  --profile multiseed \
  --n 120 --d 6 --seed 2026 --n-seeds 20 \
  --nuisance-learner xgb \
  --fd-r-g-solver direct \
  --fd-r-b-learner xgb \
  --out results/benchmark_multiseed.json

Output schema (fdcate.benchmark, schema_version=0) contains:

• clean RMSE for fd-pi, fd-dr, fd-r
• weak-overlap RMSE for fd-pi, fd-dr, fd-r
• aggregate_mean_rmse across the two scenarios
• with --profile multiseed: per_seed results + summary statistics (mean/std/min/max)

FD-R benchmarking knobs:

• --fd-r-g-solver: direct or ratio
• --fd-r-b-learner: xgb or nn
• --no-fd-r-swap-average: disable swapped D1/D2 averaging

CI also runs a golden snapshot regression test:

• tests/test_benchmark_golden.py
• golden reference file: tests/benchmark_quick_reference.json

Live Demo (Toy + Benchmark)

Primary path (CLI one-click):

fdcate demo --outdir /tmp/fdcate_live_demo

Secondary path (legacy helper script):

bash scripts/run_demo_quick.sh

The demo writes:

• /tmp/fdcate_live_demo/fit_out/summary.txt
• /tmp/fdcate_live_demo/fit_out/results.json
• /tmp/fdcate_live_demo/fit_out/diagnostics.json
• /tmp/fdcate_live_demo/fit_out/effects.csv
• /tmp/fdcate_live_demo/fit_out/model.pkl
• /tmp/fdcate_live_demo/benchmark_quick.json

Manual one-liners:

fdcate synthetic --n 120 --d 6 --seed 2026 --out /tmp/fdcate_live_demo/synthetic.csv
fdcate fit --data /tmp/fdcate_live_demo/synthetic.csv --outcome y --treat t --med m --method fd-dr --nuisance-learner xgb --outdir /tmp/fdcate_live_demo/fit_out
fdcate benchmark --n 60 --d 4 --seed 17 --nuisance-learner xgb --out /tmp/fdcate_live_demo/benchmark_quick.json

Example terminal output preview (fdcate demo --outdir /tmp/fdcate_live_demo):

[demo] output directory: /tmp/fdcate_live_demo
[demo] ATE=0.540874
[demo] generated files:
 - /tmp/fdcate_live_demo/synthetic.csv
 - /tmp/fdcate_live_demo/fit_out/summary.txt
 - /tmp/fdcate_live_demo/fit_out/results.json
 - /tmp/fdcate_live_demo/fit_out/diagnostics.json
 - /tmp/fdcate_live_demo/fit_out/effects.csv
 - /tmp/fdcate_live_demo/fit_out/model.pkl
 - /tmp/fdcate_live_demo/benchmark_quick.json
[demo] next: fdcate effect --model /tmp/fdcate_live_demo/fit_out/model.pkl --data /tmp/fdcate_live_demo/synthetic.csv --out /tmp/fdcate_live_demo/effects_from_model.csv

Final benchmark figures (FD-R full-noise setting):

Model Compatibility Policy (model.pkl)

model.pkl loading is allowed only when major.minor package versions match.

• Example: model saved with 0.1.x can be loaded by 0.1.y.
• Example: model saved with 0.1.x cannot be loaded by 0.2.x.

Scope (v0.1)

Supported:

• binary treatment T ∈ {0,1}
• binary mediator M ∈ {0,1}
• numeric covariates
• continuous or binary outcome (regression handling)

Not supported:

• non-binary T/M
• automatic categorical encoding pipelines

Legacy Reproduction Scripts

The original paper-focused scripts are preserved:

• python FDCATE.py --help
• python analyze_fars_2000_fd.py --help

Development

python -m pip install -e .[dev]
python -m pytest -q
python -m build

Nightly/manual slow tests are separated from PR fast gates:

python -m pytest -q -m "slow"

Release (v0.1.0)

bash scripts/release_preflight.sh

Detailed checklist: RELEASE_RUNBOOK.md

Troubleshooting

1. fdcate: command not found

• Re-open your shell after installation, or run with module form:
  • python -m fd_cate --help

2. XGBoost import/runtime issue

• Reinstall in a clean environment:
  • python -m pip install -U pip
  • python -m pip install --force-reinstall fd-cate

3. Permission or write-path errors

• Use a writable output directory explicitly:
  • fdcate demo --outdir /tmp/fdcate-demo