scientific-causal-ml

You can estimate heterogeneous treatment effects by applying machine learning-based causal inference methods like Double ML or meta-learners to your observational data. This Skill uses EconML and DoWhy to process treatment, outcome, and covariate variables for robust effect estimation.

The best way to use DoWhy and EconML together is to build a causal model with DoWhy for identifiability, then apply EconML methods like Causal Forest or Double ML to estimate heterogeneous treatment effects. This pipeline handles high-dimensional covariates effectively.

Yes, you can use Causal Forest and Double ML for high-dimensional covariates. These EconML-based methods are specifically designed to estimate conditional average treatment effects (CATE) by leveraging machine learning to handle complex, high-dimensional datasets.

Yes, you need scikit-learn and Python libraries like dowhy and econml to run these causal inference pipelines. These dependencies are required to process your observational data and execute the machine learning models for treatment effect estimation.

Causal feature discovery reveals treatment effect heterogeneity by identifying which covariates drive variations in outcomes across different groups. This Skill uses meta-learners and Causal Forest to interpret these features and expose the underlying heterogeneous treatment effects.

CATE estimation with meta-learners requires tabular observational data containing three distinct columns: a treatment variable, an outcome variable, and covariates. This data structure allows EconML and DoWhy to properly identify and estimate the conditional average treatment effects.