Probabilistic Programming Languages are expressive tools for exploring statistical relationships. Recently, interest has been growing in extending their capabilities to answer interventional and counterfactual queries.
In this talk, I want to give an introduction to Pearl’s framework of causality and discuss how to adapt it to the world of programming languages. I will outline different approaches, challenges and open questions and extend an invitation to the audience to explore this exciting new paradigm.
Sun 11 JanDisplayed time zone: Brussels, Copenhagen, Madrid, Paris change
Sun 11 Jan
Displayed time zone: Brussels, Copenhagen, Madrid, Paris change
11:00 - 12:30 | |||
11:00 55mKeynote | A Welcome to Causal Probabilistic Programming LAFI | ||
11:56 10mTalk | Verifying Sampling Algorithms via Distributional Invariants LAFI Daniel Zilken , Tobias Winkler RWTH Aachen University, Kevin Batz RWTH Aachen University, Joost-Pieter Katoen RWTH Aachen University Media Attached File Attached | ||
12:08 10mTalk | Sequential Monte Carlo Program Synthesis with Refinement Proposals LAFI Maddy Bowers Massachusetts Institute of Technology, Mauricio Barba da Costa MIT, Xiaoyan Wang Massachusetts Institute of Technology, Joshua B. Tenenbaum Massachusetts Institute of Technology, Vikash Mansinghka Massachusetts Institute of Technology, Armando Solar-Lezama Massachusetts Institute of Technology, Alexander K. Lew Yale University | ||
12:20 10mTalk | A Word Sampler for Well-Typed Functions LAFI Pre-print File Attached | ||