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This repository provides a collection of Python implementations for causal inference, designed to estimate the impact of specific interventions using observational data. It serves as a statistical toolkit for researchers to isolate causal signals from complex confounding factors in data sets that lack experimental control. The framework enables the application of rigorous methodologies to study health determinants and evaluate policy interventions. By utilizing structural causal modeling and directed acyclic graphs, the library allows users to map causal dependencies and identify the necessar
The main features of doubleml/doubleml-for-py are: Causal Inference.
Projects with overlapping indexed features include: jrfiedler/causal_inference_python_code — This repository provides a collection of Python implementations for causal inference, designed to estimate the impact… bookingcom/upliftml. ibm/causallib. akelleh/causality — Tools for causal analysis. py-why/dowhy — DoWhy is an open-source Python library for causal inference that structures the entire analysis into a sequential… py-why/econml — EconML is a Python library for causal inference designed to estimate heterogeneous treatment effects using a…
