We will meet to discuss topics at the intersection of algorithm design, statistics, and causal inference.
If you're interested in joining, please email arnabb@nus.edu.sg.
Schedule
| Date | Topic | Links | Speaker |
|---|---|---|---|
| June 19, 2020 | Causal Forests | Wager-Athey, Kunzel et al., Slides, Notes | Arnab Bhattacharyya |
| July 3, 2020 | Causal Identification | Huang-Valtorta, Video | Saravanan Kandasamy |
| July 24, 2020 | Intervention Design for Causal Discovery | [AKMM '20], Video | Raghav Addanki |
| July 31, 2020 | Weighting-Based Estimators | [JTB '20], Slides, Video | Sutanu Gayen |
| Aug 14, 2020 | Inference with Truncation | Slides, Video | Andrew Ilyas |
| Sep 4, 2020 | Truncated Mixtures of Gaussians | Paper, Video | Ioannis Panageas |
| Sep 25, 2020 | Learning non-parametric causal graphs | Slides, Video | Bryon Aragam |
| Oct 16, 2020 | Robust identifiability in linear structural equation model of causal inference | Paper, Video | Karthik Sankaraman |
Suggested Topics
- Efficient Intervention Design for Causal Discovery with Latents (Addanki, Kasiviswanathan, McGregor, Musco)
- Efficient Policy Learning (Athey, Wager)
- Learning linear structural equation models in polynomial time and sample complexity (Ghoshal, Honorio)
- Learning discrete Bayesian networks in polynomial time and sample complexity (Barik, Honorio)
- Estimating Causal Effects Using Weighting-Based Estimators (Jung, Tian, Bareinboim)
- On Pearl's hierarchy and the foundations of causal inference (Bareinboim, Correa, Ibeling, Icard)
- An Efficient Algorithm for Computing Interventional Distributions in Latent Variable Causal Models (Shpitser, Robinson, Robins)