Explore Causal Inference Scenarios
Start from the setup that looks closest to your problem. Each card jumps to a full workflow below with data assumptions, diagnostics, and model links.
Introduction to Causal Inference
Potential outcomes, notation, estimands, and the core intuition behind causal effects.
Classic RCT
Clean treatment-control experiments without pre-treatment outcome data.
CUPED
Randomized experiments that use pre-period signal to tighten treatment-effect estimates.
Unconfoundedness
Client-level observational studies with rich confounders, overlap checks, and robust ATE estimation.
GATE
Subgroup treatment effects built on top of an observational IRM workflow.
Multi Unconfoundedness
Observational settings with three or more treatment arms and pairwise effect contrasts.
Synthetic Control
Single treated-unit panel setups matched against a weighted synthetic donor pool.
0. Introduction to Causal Inference
Get to know notation and intuition of Causal Inference with the Potential Outcome Framework. Learn what calculation of effect could be done.
Read the full article1. Classic RCT
The Randomized Controlled Trial (RCT) is widely considered the gold standard for establishing causal relationships. By randomly assigning subjects to treatment and control groups, we ensure that both groups are identical in all aspects except for the treatment itself.
We call it classic because we do not have pre-treatment data of your units.
Clean randomization, first experiments, binary or revenue outcomes.
ATE via diff-in-means with classic inference (t-test, z-test, or bootstrap).
ATE definition, diff-in-means estimator, and inference for continuous or conversion outcomes.
- Unconfoundedness: random assignment breaks correlation with potential outcomes.
- Overlap: each unit has a non-zero probability of assignment to every arm.
- SUTVA: no interference and consistent treatment definitions.
2. CUPED
Controlled-experiment Using Pre-Experiment Data (CUPED) is a powerful variance reduction technique. By leveraging data from before the experiment started, we can reduce the variance of our metric and detect smaller effects with the same sample size.
We use CUPED when pre-treatment outcomes are available for the same units and are predictive of post-treatment outcomes.
Randomized experiments with strong pre-period signals and noisy outcomes.
CUPED-adjusted ATE with tighter confidence intervals than raw diff-in-means.
CUPED adjustment, optimal theta estimation, and adjusted treatment-effect estimator.
Sample Ratio Mismatch (SRM) checks assignment integrity before CUPED analysis.
- Unconfoundedness: assignment remains independent of potential outcomes.
- Overlap: each unit has non-zero assignment probability.
- SUTVA: no interference and consistent treatment definition.
- Pre-treatment outcomes are measured before treatment and predictive of post-period outcomes.
3. Unconfoundedness
Unconfoundedness is the assumption that we have measured all variables that influence both the treatment assignment and the outcome. This allows us to estimate causal effects from observational data by adjusting for these measured confounders.
We use this setup when treatment is not randomized and we rely on rich confounders plus overlap checks for valid identification.
Non-randomized treatment settings with strong, rich confounders and sufficient overlap.
Debiased ATE estimates with robust confidence intervals under unconfoundedness and overlap.
DML-IRM orthogonal score, cross-fitting, nuisance estimation, and robust ATE inference.
Overlap and refutation diagnostics validate support and robustness in observational settings.
- Unconfoundedness: all confounders affecting treatment and outcome are observed.
- Overlap: each unit has non-zero treatment probability conditional on covariates.
- SUTVA: no interference and consistent treatment definitions.
- Nuisance models are sufficiently accurate for orthogonalized estimation.
4. GATE
GATE extends the observational IRM workflow from one average effect to subgroup-level treatment effects. After fitting IRM, we project the orthogonal signal onto pre-defined client groups to measure how impact differs across segments.
We use this setup when subgroup definitions are chosen before treatment and we want interpretable, group-level heterogeneity instead of a single pooled estimate.
Observational studies where treatment effect heterogeneity matters and groups are defined before intervention.
Group Average Treatment Effects for mutually exclusive client segments with subgroup-level uncertainty.
Orthogonal IRM signal averaged within groups, with subgroup regression and robust inference.
- Groups must be pre-defined and aligned to the fitted observations through `user_id`.
- The subgroup basis should be mutually exclusive and exhaustive.
- Every estimable group needs at least one treated and one control observation.
- SUTVA / consistency: observed outcomes correspond to the realized treatment, with no interference across units.
- Unconfoundedness: conditional on observed covariates, treatment assignment is independent of potential outcomes.
- Overlap / positivity holds for relevant covariates and within each estimable group.
- Group membership must be pre-treatment, not defined by treatment, outcomes, or post-treatment covariates.
- Cross-fitted nuisance models are accurate enough for the orthogonal score to behave like a stable pseudo-outcome.
5. Multi Unconfoundedness
Multi Unconfoundedness extends observational identification to multiple treatment arms. We estimate causal contrasts across arms by adjusting for observed confounders and modeling generalized propensity scores.
We use this setup when assignment is non-random and treatment has three or more levels.
Multi-arm non-randomized interventions with rich covariates and sufficient overlap across arms.
Pairwise and baseline-referenced treatment effects with orthogonalized robust inference.
Multi-treatment IRM score construction, cross-fitting, and robust effect inference across arms.
Overlap and balance diagnostics ensure identification support across all treatment arms.
- Multi-arm unconfoundedness: all confounders affecting arm assignment and outcomes are observed.
- Overlap: each unit has positive probability for every treatment arm.
- SUTVA: no interference and consistent treatment definitions across arms.
- Nuisance models for outcome and generalized propensity are sufficiently accurate.
6. Synthetic Control
Synthetic Control estimates treatment effects for one treated unit by constructing a weighted combination of untreated units that matches pre-treatment dynamics.
We use this setup when treatment happens at a unit-time level and pre-treatment trajectories are rich enough to build a credible synthetic counterpart.
Single-treated-unit panel settings with strong untreated donor pools and informative pre-periods.
Post-treatment effect path and aggregate ATTE from treated vs synthetic trajectories.
Augmented Synthetic Control formulation with bias correction and post-treatment effect aggregation.
Placebo and pre-period fit checks validate synthetic control quality and support interpretation.
- One treated unit and untreated donor units remain unaffected by treatment spillovers.
- Pre-treatment outcomes are informative enough to approximate the treated counterfactual.
- No structural break unrelated to treatment invalidates post-treatment comparisons.