Roni Kobrosly Ph.D.'s Website

A python package for performing causal inference when a treatment is continuous.

There are many implemented methods to perform causal inference when your intervention of interest is binary, but few methods exist to handle continuous treatments.

This is unfortunate because there are many scenarios (in industry and research) where these methods would be useful. For example, when you would like to:

• Estimate the causal response to increasing or decreasing the price of a product across a wide range.
• Understand how the number of minutes per week of aerobic exercise causes positive health outcomes.
• Estimate how decreasing order wait time will impact customer satisfaction, after controlling for confounding effects.
• Estimate how changing neighborhood income inequality (Gini index) could be causally related to neighborhood crime rate.

This library attempts to address this gap, providing tools to estimate causal curves (AKA causal dose-response curves). Both continuous and binary outcomes can be modeled against a continuous treatment.

The GitHub repository can be found here.