Causal Matrix Estimation
Talkat Algorithmic Advances for Statistical Inference with Combinatorial Structure · 2021-10-11..10-15·
summary
Estimates a low‑rank matrix A when the observation mask D depends on latent row/column factors (a causal observation mask that can confound sampling but not depend on per‑entry noise). Presents a concrete algorithm, synthetic nearest neighbor, that 1) finds a fully observed local square (assumes a fully observed K×K square exists), 2) denoises it by SVT/PCR, 3) fits local linear weights like synthetic control and averages many such rectangles. Shows USVT and SoftImpute produce large histogram bias under MCAR/MNAR while the proposed method gives better entrywise error and averaging/CLT behavior when K grows.
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chapters (18)
- 0:00Intro
- 0:47Outline
- 4:37Matrix Completion: Examples
- 8:29Matrix Completion: Model
- 14:11Why is it limited MNAR?
- 21:55Review: Algorithms
- 22:29Review: MCAR Guarantees
- 24:34Comparison: MCAR
- 27:23Comparison: Limited MNAR
- 28:59Comparison: Causal MNAR
- 31:46Nearest Neighbors (NN)
- 32:57Synthetic Control (SC)
- 36:36Robust Synthetic Control (RSC)
- 37:07Synthetic Nearest Neighbors = NN + RSC
- 41:37Results
- 43:36Experiment: Synthetic Setup
- 43:59Experiment: Panel Data
- 45:51Causal Inference and Matrix Completion
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