Skip to main content

Python implementation of causal trees with validation

Project description


Christopher Tran, Elena Zheleva, "Learning Triggers for Heterogeneous Treatment Effects", AAAI 2019.

Our method is based on and adapted from:


  • Python 3
  • sklearn
  • scipy
  • graphviz (if you want to plot the tree)


pip install causal_tree_learn

Demo Code

Two demo codes are available to run.


Runs the tree on a binary example (asthma.txt)


Runs a tree on a trigger problem where the treatment is continuous (note for now the example is made up and treatment does not affect outcome, this is only to show example code)

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for causal-tree-learn, version 1.0.14
Filename, size File type Python version Upload date Hashes
Filename, size causal_tree_learn-1.0.14-py3-none-any.whl (13.0 kB) File type Wheel Python version py3 Upload date Hashes View hashes
Filename, size causal_tree_learn-1.0.14.tar.gz (11.2 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page