Causal Inference & Deep Learning, MIT IAP 2018
Taught by Fredrik Johansson and Max Shen. Organized by Max Shen.
We would like everyone who have taken part in the course to fill out a short evaluation form.
Evaluation form: https://docs.google.com/forms/d/e/1FAIpQLSdA0ogPvj-dXZ7IfcbsOP5UAqNaFUPoA8Vwx_156x80uMGLnw/viewform
1. Tuesday, January 16th: 5pm-6:30pm at Room 4-231
- Causal Models and Statistical Models (MS)
- Structural Causal Models and Interventional Distributions (MS)
- Potential Outcomes Framework (FJ)
- Counterfactual Inference (FJ)
- Causal Effects (FJ)
- Conditional Treatment Effects (FJ)
- Distributional Shift (FJ)
- Domain Adaptation (FJ)
2. Wednesday, January 17th: 5pm-6:30pm at Room 4-231
- Counterfactual Inference, continued (FJ)
- Importance Sampling (FJ)
- Model Misspecification (FJ)
- Potential Outcomes and Deep Style Transfer (MS)
- Cause-Effect Discovery with... (MS)
- Additive Noise Models and the Hilbert-Schmidt Independence Criterion
- Convolutional Neural Nets
- Conditional GANs
- Randomized Causation Coefficient
- Proxy Variables
- Semi-Supervised Learning and Causality
3. Thursday, January 18th: 5pm-6:30pm at Room 4-231
- Causal Aspects of Reinforcement Learning (FJ)
- Policy Optimization (FJ)
- Off-Policy Evaluation (FJ)
- Batch Reinforcement Learning (FJ)
Available as files in this repository.
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Johansson, F. D., Shalit, U., & Sontag, D. (2016). Learning Representations for Counterfactual Inference. http://arxiv.org/abs/1605.03661
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Peters, J. (2017). Elements of Causal Inference (Draft).
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Lopez-paz, D., & Sch, B. (2015). Towards a Learning Theory of Cause-Effect Inference. Proceedings of the 32nd International Conference on Machine Learning.
For a complete list of references, refer to lecture notes and slides.