Causality and Casual Machine Learning

The proposal application period for the 2021 HPCC Systems intern Program is now open.

The deadline date for proposal applications is Friday 19th March 2021.

Discuss your ideas with the project mentor and send your final proposal to Lorraine Chapman.

This project is no longer available as a student work experience opportunity with HPCC Systems in 2021. Curious about other projects we are offering? Take a look at our Ideas List. 

Find out about the HPCC Systems Summer Internship Program.

Project Description

We want to evaluate the use of Causality algorithms as described in Causal Inference in Statistics by Judea Pearl et al.

Student will collaboratively identify appropriate datasets for testing, and will produce tested production code implementing the selected algorithms.

Completion of this project involves:

• Survey latest techniques and research for Causal Modeling and Causal Inferencing

• Develop tools for Causal Modeling  including:
  1. Statistical Independence and Conditional Independence Tests
  2. Causal Modeling representation
  3. Model Identifiability Detection
  4. Interventional Calculus solver
  5. Counterfactual Calculus solver
• Causal Modeling User Interface
  1. Identify multiple datasets for real-world analysis
  2. Conduct analysis of selected datasets and compare with known ground truth where available
• Publish paper(s) on the research
• Test and document code for general release

By the mid term review we would expect you to have:

• TBC

We want to evaluate the use of Causality algorithms as described in “Causal Inference in Statistics” by Judea Pearl et al.

-        Survey latest techniques and research for Causal Modeling and Causal Inferencing

-        Develop tools for Causal Modeling  including:

o   Statistical Independence and Conditional Independence Tests

o   Causal Modeling representation

o   Model Identifiability Detection

o   Interventional Calculus solver

o   Counterfactual Calculus solver

o   Causal Modeling User Interface

-        Identify multiple datasets for real-world analysis

-        Conduct analysis of selected datasets and compare with known ground truth where available

-        Publish paper(s) on the research

-        Test and document code for general release

The successful applicants will demonstrate:

-        Proficiency with probability and statistics

-        Understanding of the basic concepts and techniques of Causality and Causal Inference

-        Experience with Machine Learning algorithms