# Causal Matrix Completion

@article{Agarwal2021CausalMC, title={Causal Matrix Completion}, author={Anish Agarwal and Munther A. Dahleh and Devavrat Shah and Dennis Shen}, journal={ArXiv}, year={2021}, volume={abs/2109.15154} }

Matrix completion is the study of recovering an underlying matrix from a sparse subset of noisy observations. Traditionally, it is assumed that the entries of the matrix are “missing completely at random” (MCAR), i.e., each entry is revealed at random, independent of everything else, with uniform probability. This is likely unrealistic due to the presence of “latent confounders”, i.e., unobserved factors that determine both the entries of the underlying matrix and the missingness pattern in the…

## 10 Citations

### Truncated Matrix Completion - An Empirical Study

- Computer Science2022 30th European Signal Processing Conference (EUSIPCO)
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Through a series of experiments, this paper studies and compares the performance of various LRMC algorithms that were originally successful for data-independent sampling patterns and considers various settings where the sampling mask is dependent on the underlying data values.

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### CausalSim: Unbiased Trace-Driven Network Simulation

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Estimation of CausalSim on both real and synthetic datasets and two use cases shows that it provides accurate counterfactual predictions, reducing prediction error by 44% and 53% on average compared to expert-designed and supervised learning baselines.

### Optimal Recovery for Causal Inference

- Economics
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We propose a generalization of the synthetic control and synthetic interventions methodology to the dynamic treatment regime. We consider the estimation of unit-specific treatment effects from panel…

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### Extrapolating missing antibody-virus measurements across serological studies.

- BiologyCell systems
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This work designs two-phase bandit algorithms that first use subsampling and low-rank matrix estimation to obtain a substantially smaller targeted set of products and then apply a UCB procedure on the target products to find the best one.

### Counterfactual inference for sequential experimental design

- MathematicsArXiv
- 2022

We consider the problem of counterfactual inference in sequentially designed experiments wherein a collection of N units each undergo a sequence of interventions for T time periods, based on policies…

### Counterfactual inference for sequential experiments

- Computer Science, Mathematics
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A latent factors model is introduced over the counterfactual means that serves as a non-parametric generalization of the non-linear mixed effects model and the bilinear latent factor model considered in prior works to provide inference guarantees for thecounterfactual mean at the smallest possible scale.

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