Causal Inference Method Selector
How to choose a causal inference method: the basics.
Choose causal inference methods from study design, identification strategy, and business objective.
This tool uses a decision-tree backbone centered on identification structure, but it returns multiple viable methods with assumptions and follow-up checks rather than forcing a single branch.
Study Setup
Answer the questions that matter for identification. The tool will adapt the later questions to your design.
Start with whether treatment assignment was randomized or not.
Pick the main causal question, not every downstream analysis you may run later.
Design Signals
Examples: pre-period spend, trips, clicks, or repeated baseline outcome measurements.
Use this only for experimental settings.
Examples: shared driver supply, seller liquidity, auction budgets, inventory competition, or social-network spillovers.
Example: assigned users do not always adopt the feature, or encouragement differs from uptake.
Needed for methods such as difference-in-differences, interrupted time series, and synthetic control.
Examples: one city launch, one state regulation, one platform-wide intervention.
Examples: age cutoff, credit score threshold, policy eligibility boundary.
The instrument must shift treatment strongly and affect the outcome only through treatment.
If treated and untreated units barely overlap in covariate space, many adjustment methods become unstable.
Think high-cardinality features, rich user history, text, or many confounders.
Recommended Methods
The tool shows a primary recommendation, strong fallbacks, and identification warnings.
Why this is not a rigid one-path decision tree
- Many applied problems support more than one defensible method.
- Identification assumptions matter more than the algorithm name.
- Practitioners often need a primary method plus a robustness check, not a single branch answer.
- The best workflow is usually design first, estimator second, diagnostics third.
This selector therefore uses a decision-tree backbone but returns method cards with fit, assumptions, and what to validate next.
Methods covered
- Randomized experiment analysis with covariate adjustment
- Switchback experiments for interference-heavy marketplaces or networks
- CUPED / pre-period variance reduction
- Effect among compliers (CACE / LATE) via IV for noncompliance
- Heterogeneous treatment effect models such as causal forests, uplift models, and meta-learners
- Mediation analysis
- Matching and propensity-score weighting
- Doubly robust estimators such as AIPW and double machine learning
- Difference-in-differences and event-study style designs
- Interrupted time series and synthetic control
- Regression discontinuity design
- Instrumental variables for observational settings