Causal Inference Analysis Workflow
Design and validate causal inference studies with methodological rigor
What You Can Do
This skill guides you through rigorous causal inference workflows, from study design through effect estimation and sensitivity analysis. You'll select appropriate methodologies (RCTs, propensity score matching, instrumental variables, difference-in-differences), validate causal assumptions, and interpret results with proper uncertainty quantification. It helps you move beyond correlation to credible causal claims supported by statistical evidence.
Features
Get guidance on choosing between RCTs, observational methods, matching approaches, IV estimation, and quasi-experimental designs based on your data and research question
Systematically check causal assumptions like unconfoundedness, overlap/common support, exclusion restrictions, and parallel trends with diagnostics and robustness tests
Design and evaluate propensity score models, assess balance across covariates, apply matching/stratification/weighting, and diagnose positivity violations
Estimate ATE, ATT, CATE, and heterogeneous treatment effects with proper standard errors, confidence intervals, and multiple imputation for missing data
Quantify robustness to unmeasured confounding, specification choices, and sample selection with formal sensitivity analyses and bounds
Translate technical findings into actionable insights, identify causal mechanisms, discuss limitations transparently, and present uncertainty appropriately
Get reproducible analysis templates in R, Python, or Stata with comments explaining each step, assumption checks, and output interpretation
Example Output
Example 1: Study Design Recommendation
Research Question: Does job training increase earnings?
Data: Observational sample of program participants and non-participants
Suggested Approach: Propensity score matching with sensitivity analysis
- Check: Unconfoundedness assumptions (discuss potential unmeasured confounders)
- Diagnostics: Covariate balance before/after matching, common support region
- Estimation: ATT with bootstrapped 95% CI
- Robustness: Rotnitzky bounds for hidden bias
Example 2: Assumption Validation
✓ Overlap: 95% of treated units in common support region
✓ Balance: Standardized differences <0.1 for all pre-treatment covariates
✗ Parallel Trends: Pre-trend difference = 0.15, p=0.08 (marginal concern)
→ Recommendation: Include placebo outcome test and robustness check with alternative parallel periods
Example 3: Treatment Effect with Uncertainty
Average Treatment Effect: $2,850/year [95% CI: $1,200–$4,500]
Effect size: 0.35 SD; Practical significance: Moderate
Robustness: Estimate stable across 8 specification choices
Caveat: Sensitive to unmeasured confounding >1.5x observed bias
What's Included
- Causal Inference Decision Tree: Interactive flowchart to navigate from research question to appropriate methodology, accounting for data type, study design, and key constraints
- Assumption Checklist Library: Pre-built diagnostic frameworks for each major methodology (RCT, matching, IV, DID, RDD) with specific tests and remedies
- Analysis Templates: Reproducible code templates in R/Python/Stata for propensity score estimation, balance checking, effect estimation, and sensitivity analysis with inline documentation
- Robustness Testing Protocols: Structured approaches to sensitivity analysis, including bounds methods, specification sweeps, and tests for unmeasured confounding
- Results Communication Guide: Templates for translating technical results into clear narratives, including visualization recommendations and honest discussion of limitations
Who It's For
- Econometricians & Policy Researchers
- Data Scientists in Tech (A/B testing design, feature impact analysis)
- Healthcare Researchers (observational study design)
- Academic Researchers (thesis design, publication-ready analysis)
- Impact Evaluation Specialists
Best For
- Designing rigorous observational studies
- Validating causal assumptions before analysis
- Selecting appropriate causal inference methodology
- Conducting sensitivity and robustness analysis
- Interpreting and communicating causal findings
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