Econometrics and Causal Inference Expert-Econometric & Causal Inference Analysis Tool

Example

Transforming the policy question "Did school voucher program A improve test scores?" into an estimand such as the local average treatment effect (LATE) for compliers when eligibility is randomized imperfectly, or the average treatment effect on the treated (ATT) when analyzing participants.

Scenario

A ministry reports participant/outcome data but lacks a clean control group. The Expert inspects the rollout (eligibility rules, cutoffs, timing), recommends whether RCT, RD, DiD, IV, or matching is appropriate, and writes the explicit estimating equation and assumptions (e.g., conditional independence, exclusion restriction, continuity at cutoff).

Example

Applying two-stage least squares (2SLS) when there is endogenous selection into treatment, implementing difference-in-differences with event-study specifications and clustered standard errors, or using double/debiased machine learning (DML) to estimate heterogeneous treatment effects with many covariates while controlling for overfitting.

Scenario

A tech firm wants to estimate the causal effect of a new app feature rollout that was A/B tested but with imperfect compliance. The Expert provides code templates for 2SLS and DML, explains when to cluster and by which unit (user, region), and demonstrates how to compute robust standard errors, bootstrap confidence intervals, and bias corrections for small samples.

Example

Running pre-trends tests and placebo treatment windows in DiD; McCrary density and continuity checks in RDD; weak-instrument tests and overidentification (Sargan/Hansen) tests for IV; Rosenbaum bounds or E-value calculations for unobserved confounding; and leave-one-out/synthetic control weight sensitivity.

Scenario

A published study claims a large effect from a minimum-wage increase. The Expert runs an event-study to test parallel trends, performs falsification tests on unaffected age groups, calculates how strong an unobserved confounder would have to be to overturn results (E-value), and produces a robustness table showing estimates under alternative control sets, functional forms, and bandwidths.

Example

Drawing and interpreting Directed Acyclic Graphs (DAGs) to identify confounders, mediators, colliders, and appropriate adjustment sets; using do-calculus intuition to show why conditioning on a collider induces bias.

Scenario

In a health study where treatment assignment depends on both observed symptoms and an unobserved genetic trait, the Expert helps the researcher draw a DAG, identify variables that must not be conditioned on (colliders), and propose an identification strategy (e.g., instrumenting with policy variation) that avoids collider bias.

Example

Estimating conditional average treatment effects (CATEs) using tree-based methods (causal forests), or semi-parametric interaction models to reveal subgroups benefitting most from an intervention.

Scenario

A bank wants to target a retention incentive. The Expert shows how to estimate CATEs with causal forests, interprets feature importance, warns about overfitting and data-snooping, and proposes an A/B rollout to validate targeting rules before full-scale deployment.

Example

Providing annotated code (R/Python/stata) for data cleaning, estimation, diagnostics, and figures; producing a results table with standard errors, p-values, and sensitivity metrics; and writing a concise interpretation for policymakers emphasizing identification assumptions and policy-relevant magnitudes (e.g., percentage point changes).

Scenario

A researcher needs a replicable appendix for a journal submission. The Expert generates a step-by-step script, creates event-study plots and balance tables, and drafts the methods appendix text describing assumptions, estimation choices, and robustness checks.