laurencium/Causalinference

Causal Inference in Python

What it solves

It provides a set of statistical and econometric tools to perform causal inference, program evaluation, and treatment effect analysis, allowing users to determine the impact of a specific treatment or intervention on an outcome.

How it works

The package implements various methods for estimating treatment effects through techniques such as matching, blocking, weighting, and least squares. It also includes tools for assessing covariate distribution overlap, estimating propensity scores, and improving covariate balance via trimming and subclassification.

Who it’s for

Researchers and analysts in fields like econometrics and statistics who need to estimate the impact of interventions using Python.

Highlights

  • Propensity score estimation and subclassification
  • Treatment effect estimation via matching, blocking, and weighting
  • Covariate balance improvement through trimming
  • Assessment of overlap in covariate distributions

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