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Causal Matrix Estimation

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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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  1. 0:00Intro
  2. 0:47Outline
  3. 4:37Matrix Completion: Examples
  4. 8:29Matrix Completion: Model
  5. 14:11Why is it limited MNAR?
  6. 21:55Review: Algorithms
  7. 22:29Review: MCAR Guarantees
  8. 24:34Comparison: MCAR
  9. 27:23Comparison: Limited MNAR
  10. 28:59Comparison: Causal MNAR
  11. 31:46Nearest Neighbors (NN)
  12. 32:57Synthetic Control (SC)
  13. 36:36Robust Synthetic Control (RSC)
  14. 37:07Synthetic Nearest Neighbors = NN + RSC
  15. 41:37Results
  16. 43:36Experiment: Synthetic Setup
  17. 43:59Experiment: Panel Data
  18. 45:51Causal Inference and Matrix Completion
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