Causal inference estimators

CausalEstimate provides IPW, AIPW, TMLE, and propensity-score matching for observational data. All estimators share the same pattern: configure columns and effect type in the constructor, then call compute_effect(df).

Supported effect types

Estimator	ATE	ATT	RR	RRT	ARR
IPW	✓	✓	✓	✓	✓
AIPW	✓	✓	–	–	✓
TMLE	✓	✓	✓	–	✓
Matching	✓*	–	–	–	✓*

ATE: average treatment effect · ATT: ATE on the treated · RR: risk ratio · RRT: risk ratio on the treated · ARR: absolute risk reduction.

* With a caliper, the matched population is strictly neither the full nor the treated population; interpret accordingly.

Continuous outcomes. ATE and ATT work for any numeric outcome in IPW, AIPW and TMLE; the risk-based effect types (RR, RRT, ARR) require a binary 0/1 outcome. TMLE rescales a continuous outcome and its predictions to [0, 1] using the observed min/max before targeting and maps the result back; pass y_bounds=(min, max) to use known bounds instead.

IPW — inverse probability weighting

Weights each observation by the inverse of its (estimated) probability of receiving the treatment it actually received. Only needs a propensity score column. Pass stabilized=True for stabilized weights.

from CausalEstimate.estimators import IPW

ipw = IPW(effect_type="ATE", treatment_col="treatment", outcome_col="outcome", ps_col="ps")
result = ipw.compute_effect(df)

AIPW — augmented IPW (doubly robust)

Combines the propensity score with outcome-model predictions. Consistent if either the propensity model or the outcome model is correctly specified.

from CausalEstimate.estimators import AIPW

aipw = AIPW(
    effect_type="ATE",
    treatment_col="treatment",
    outcome_col="outcome",
    ps_col="ps",
    probas_t1_col="predicted_outcome_treated",
    probas_t0_col="predicted_outcome_control",
)

TMLE — targeted maximum likelihood estimation

Doubly robust like AIPW, but updates the initial outcome predictions with a targeting step, which typically improves finite-sample behavior. Additionally requires predictions under the observed treatment.

from CausalEstimate.estimators import TMLE

tmle = TMLE(
    effect_type="ATE",
    treatment_col="treatment",
    outcome_col="outcome",
    ps_col="ps",
    probas_col="predicted_outcome",
    probas_t1_col="predicted_outcome_treated",
    probas_t0_col="predicted_outcome_control",
)

Matching

Estimates effects by comparing matched treated/control pairs on the propensity score. See Matching for the standalone matching functions and their options.

Weight clipping

IPW, AIPW and TMLE all accept clip_percentile (default 1, no clipping) and eps (default 1e-9). clip_percentile=0.99 clips inverse-probability weights at their 99th percentile, which tames extreme propensity scores at the cost of some bias. Set the same value on every estimator in a MultiEstimator so their results stay comparable.

See the API Reference for full signatures.