causal-inference

name

causal-inference

description

Use as the entry point for any causal inference or treatment-effect question when the right method is not yet decided. This router inspects the user's data shape (cross-sectional vs panel, randomized vs observational, sample size) and business goal (average effect vs who-to-target), then recommends and dispatches to the appropriate sub-skill — PSM (psm-causal-inference), IPW (ipw-causal-inference), AIPW/doubly-robust (aipw-causal-inference), DID (did-causal-inference), or Uplift modeling (uplift-modeling). Triggers include 评估某个动作/投放/政策的因果效果, incrementality, lift, treatment effect, ATE, ATT, CATE, 该对谁投放 (who-to-target), A/B test analysis with confounding, observational treatment-effect studies, policy evaluation, and questions like 用哪种因果推断方法 / which causal method should I use. Prefer this skill first whenever the user uploads data and asks for a causal effect without naming a specific method.

Causal Inference Method Router

This is the entry-point skill for causal inference. When a user wants to estimate a causal / treatment effect but has not committed to a specific method, use this skill to (1) diagnose their data and goal, (2) recommend the best-fit method, and (3) hand off to the matching sub-skill. The five sub-skills are:

Sub-skill	Method	Estimand	Core data requirement
psm-causal-inference	Propensity Score Matching	ATT (also ATE)	Cross-sectional observational data, measured confounders
ipw-causal-inference	Inverse Probability Weighting	ATE / ATT	Cross-sectional observational data, good overlap
aipw-causal-inference	Augmented IPW / Doubly Robust	ATE / ATT	Observational data; want robustness to model misspecification
did-causal-inference	Difference-in-Differences	ATT	Panel / repeated cross-section, pre & post periods, a control group
uplift-modeling	Uplift Modeling (CATE)	Individual/subgroup effect (CATE)	Treatment + outcome, ideally randomized; goal is targeting

Workflow

1. Clarify the causal question before anything else. Establish, in plain language:

   • Treatment / intervention T (the action whose effect we want).
   • Outcome Y and the unit of analysis (user, order, store, region…).
   • Eligible population and the time window.
   • The decision the answer will inform — this determines the estimand.

2. Determine the estimand — this is the first fork.

   • "What is the average effect of the treatment?" → ATE / ATT → consider PSM, IPW, AIPW, DID.
   • "Who should I treat / who responds most?" → individual/subgroup effect (CATE) → Uplift modeling.
   • If the user wants both, estimate the average effect first, then layer Uplift for targeting.

3. Inspect the data shape — the second fork. Ask for or infer:

   • Assignment mechanism: randomized/quasi-random (A/B test) vs observational (self-selected).
   • Time structure: a single snapshot (cross-section) vs before-and-after observations for the same/comparable units (panel or repeated cross-section).
   • Is there a clean control group and a pre-period? (decisive for DID).
   • Sample size, number of confounders, and outcome type (binary/continuous).

4. Apply the recommendation logic (see the decision guide below) and state why the chosen method fits — naming the assumption it relies on and the one it cannot rescue.

5. Hand off to the chosen sub-skill for the detailed workflow, diagnostics, and a runnable example.py. Each sub-skill ships an EXPLANATION.md (Chinese, with English terms) explaining its principle, assumptions, and pitfalls.

6. When two methods are viable, recommend a primary plus a robustness check. Triangulating across methods (e.g. PSM vs IPW, or IPW vs AIPW) and showing the estimate is stable is far more convincing than a single number.

Recommendation Logic (decision guide)

Walk these checks in order; the first match is the primary recommendation.

1. Goal is targeting / heterogeneous effect ("对谁投放", who are the persuadables, individual ROI)? → Uplift modeling. Works best on randomized/A-B data; on observational data, de-confound first (PSM/IPW/AIPW) before modeling CATE.

2. Do you have before-and-after data for a treated group AND a comparable control group (panel or repeated cross-section), e.g. a policy/feature went live on a date? → DID. Strongest reason to choose it: it differences out time-invariant unobserved confounders, which the propensity-based methods cannot touch. Requires the parallel trends assumption — check pre-trends.

3. Only a single cross-sectional snapshot, observational (non-random) treatment, and you want an average effect? Then it is a propensity-score family problem — pick among the three by sub-goal:

   • Want an interpretable matched cohort / ATT, or need to show stakeholders "comparable treated vs untreated units"? → PSM. Discards non-overlapping units; intuitive but loses sample.
   • Want ATE over the whole population using all data, and overlap is adequate? → IPW. Uses every unit via weights; watch for extreme weights / positivity.
   • Want robustness to getting a model wrong, or you want to use ML models for nuisance functions? → AIPW (doubly robust). Consistent if either the propensity model or the outcome model is correct; use cross-fitting. This is the safest default among the three when you can build both models.

4. Randomized experiment (clean A/B test) and you only need the average effect? A simple difference in means is already unbiased — causal machinery is optional. Use IPW/AIPW only to correct imperfect randomization or attrition, or move to Uplift if the question is targeting.

Quick reference table

Situation	Primary	Robustness check
Policy/feature launched on a date, have pre/post + control	DID	Event-study / placebo pre-trend
Observational snapshot, want ATT, need interpretability	PSM	IPW on same data
Observational snapshot, want population ATE	IPW	AIPW
Observational snapshot, unsure which model is right	AIPW	PSM or IPW
"Who should we target?" / budget-constrained outreach	Uplift	De-confound (IPW/AIPW) first if observational
Clean randomized A/B, want average effect	Difference in means	IPW for imperfect randomization

Reporting Checklist

• The causal question: treatment, outcome, unit, population, time window, and the decision it informs.
• The estimand (ATE / ATT / CATE) and why it matches the decision.
• Data diagnosis: assignment mechanism, time structure, control group, sample size, overlap.
• The recommended method and the assumption it leans on plus the bias it cannot fix.
• At least one robustness/triangulation result when feasible.
• A clear statement of residual risk (unmeasured confounding, weak overlap, untestable parallel trends).

Guardrails

• No method fixes a bad design. If treatment is entangled with the outcome's cause and there is no overlap, no pre-period, and no randomization, say the causal question is not answerable from this data.
• Match the estimand to the decision, not to convenience. A population ATE answers a different question than "who should I target."
• DID's parallel trends and the propensity family's unconfoundedness are different escape hatches. DID can absorb time-invariant unobserved confounders but needs panel data; PSM/IPW/AIPW only adjust for observed confounders but work on a single snapshot.
• Predictive accuracy of a propensity or outcome model is not the goal — covariate balance (PSM/IPW), double robustness (AIPW), or ranking quality / Qini (Uplift) are.
• On observational data, never build Uplift CATE without first addressing confounding — heterogeneous effects estimated on biased data are biased per subgroup.
• Always hand off to the specific sub-skill for the detailed, method-correct workflow rather than improvising the estimation here.