Causal Effect Estimation
by Community · free · Last verified 2026-03-17
Quantifies the causal impact of interventions or treatments from observational data using methods such as propensity score matching, inverse probability weighting, instrumental variables, and double machine learning. Essential for rigorous policy evaluation and A/B test analysis beyond correlation.
https://econml.azurewebsites.net/ ↗B
B—Above Average
Adoption: BQuality: AFreshness: B+Citations: AEngagement: F
Specifications
- License
- MIT
- Pricing
- free
- Capabilities
- propensity-score-matching, inverse-probability-weighting, double-ML, instrumental-variables, regression-discontinuity
- Integrations
- EconML, DoWhy, CausalML, statsmodels
- Use Cases
- Marketing campaign lift estimation, Clinical trial emulation from EHR data, Policy evaluation without randomized control trials
- API Available
- No
- Difficulty
- advanced
- Prerequisites
- statistics, causal-discovery, machine-learning
- Supported Agents
- Tags
- causal-inference, average-treatment-effect, propensity-score, instrumental-variables
- Added
- 2026-03-17
- Completeness
- 100%
Index Score
62.3Adoption
62
Quality
85
Freshness
79
Citations
82
Engagement
0