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name: data-preparation description: "Prepare causalml datasets, encodings, propensity scores, matching samples, and balance checks." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

data-preparation

Use this operating sub-skill when the task is about preparing data for CausalML workflows before causal estimation, tree modeling, deep modeling, validation, or decision optimization.

Route here for

• Generating CausalML synthetic datasets for binary treatment simulations or uplift-classification examples.
• Building feature matrices with the bundled rare-category label and one-hot encoders.
• Estimating binary propensity scores with elastic-net logistic regression or gradient-boosted propensity models.
• Performing nearest-neighbor propensity-score matching with optional calipers, replacement, ratios, and exact group strata.
• Running matched-sample balance checks with standardized mean differences.
• Producing matched CSV files with the bundled scripts/match_csv.py helper.

Route elsewhere

• Meta-learners, TMLE, IV/DRIV, ATE/ITE/CATE estimation, or serialization: use ../causal-estimation/.
• Causal trees, causal forests, uplift trees, or tree visualization: use ../tree-models/.
• Neural DragonNet or CEVAE models: use ../deep-models/.
• Uplift metrics, validation, sensitivity analysis, feature selection, or treatment optimization: use ../analysis-and-decision/.

Operating map

User goal	Primary runtime artifact
Generate synthetic regression or uplift-classification data	references/workflows.md#synthetic-data-workflows
Convert mixed pandas columns into a numeric model matrix	references/workflows.md#feature-matrix-and-encoding-workflows
Estimate clipped binary propensity scores	references/workflows.md#propensity-score-workflows
Match treated and control units, including group-stratified matching	references/workflows.md#nearest-neighbor-matching-workflows
Check pre/post balance with standardized mean differences	references/workflows.md#balance-table-workflow
Prepare CSV data for matching from the shell	scripts/match_csv.py
Confirm accepted column shapes and labels	references/data-contracts.md
Diagnose common data-preparation failures	references/troubleshooting.md

Default safety checks

Before fitting or matching, confirm these items:

1. Treatment is binary 0/1 for propensity and matching APIs.
2. Treatment, outcome, observed propensity, true effects, and post-treatment variables are excluded from feature columns unless the user explicitly asks for an audit.
3. Matching score columns are numeric and do not contain missing values.
4. NearestNeighborMatch(replace=False) receives exactly one score column; use replace=True for multi-column nearest-neighbor matching.
5. Balance tables use numeric covariates; encode categorical variables or audit them separately.
6. Synthetic examples generated by synthetic_data() draw from NumPy global randomness, so set np.random.seed(...) before calling if reproducibility matters.

Bundled helper

Run the CSV helper with:

python sub-skills/data-preparation/scripts/match_csv.py \
  --input input.csv \
  --output matched.csv \
  --treatment-column treatment \
  --feature-columns age tenure spend_segment \
  --matching-covariates age tenure \
  --score-column propensity \
  --caliper 0.2 \
  --ratio 1 \
  --random-state 42

The helper imports installed CausalML APIs, writes a matched CSV, and prints balance tables before and after matching when the requested balance covariates support them.