The proposal period for 2022 internships is now open
Submit your final proposal to Lorraine Chapman before Friday 18th March 2022
These places are already taken for the 2022 program
If you are interested in this project contact Lorraine Chapman.
Find out about the HPCC Systems Summer Internship Program.
Our Causality 2021 project involved completing research designed to help us understand the latest developments in Causal Sciences, evaluate the effectiveness and limitations of the current state-of-the-art, push the boundaries of causal inferencing, and develop a full-bodied Causality Toolkit for HPCC Systems. Three student joined the 2021 HPCC Systems Intern Program to complete projects contributing to this effort in the following areas:
- Probabilities and Conditional Probabilities
- Counterfactual and Interventional Layers
- Independence, Conditional Independence and Directionality
The new HPCC Systems Causality toolkit provides a broad set of capabilities for Causal Analysis of datasets. These include:
- Causal Model Validation
- Causal Inference (Interventional and Counterfactual).
Causality 2022 Project
Our focus in 2022 involves applying the toolkit to demonstrate value against real-world datasets. We are looking for two interns to identify candidate datasets, define appropriate analytics, perform causal analysis, and publish results.
Candidates should have a basic understanding of Causal Science (ala Judea Pearl), and a good grasp of probability and statistics. Additional education in Causality and Statistics will be available for interested candidates.
Some examples of project topics might include:
- Proposal and validation of causal models for given datasets.
- Identification of Causal Metrics such as Average Causal Effect, Direct Causal Effects and Indirect Causal Effect.
- Data Transportability between studies
- Use of instrumental and / or mediation variables to infer causal effects
If you are interested in this project, please contact the mentor shown below.
More information about the HPCC Systems Causality Toolkit is available in our blog Causality 2021.
Causality Algorithm Development
This is an additional project available for internships in 2022.
Tasks involved include the following:
- Test and compare alternate Causal algorithms and packages to determine best-of-breed technologies.
- Compare algorithms for Causal Discovery, Causal Model Validation, and Causal Inference.
The work involves developing test cases and comparing results using various in-house and public Causal Analysis packages. The student will design tests, perform tests using different packages, and document their results. Assessment of algorithms and packages will be both qualitative and quantitative, and will include run-time performance as well as accuracy.
The successful candidate will have a strong background in statistics, machine learning, and preferably knowledge of Causal Science, Causal algorithms and Causal analysis packages.
Backup Mentor: Lili Xu
Student Posters from the 2021 Causality Project:
Student Blog Journals