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MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms

MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms

4 November 2021
Trent Kyono
Yao Zhang
Alexis Bellot
M. Schaar
    CML
ArXiv (abs)PDFHTML

Papers citing "MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms"

17 / 17 papers shown
Title
Prediction Models That Learn to Avoid Missing Values
Prediction Models That Learn to Avoid Missing Values
Lena Stempfle
Anton Matsson
Newton Mwai
Fredrik D. Johansson
161
0
0
06 May 2025
New User Event Prediction Through the Lens of Causal Inference
New User Event Prediction Through the Lens of Causal Inference
H. Yuchi
Shixiang Zhu
Li Dong
Yigit M. Arisoy
Matthew C. Spencer
77
0
0
08 Jul 2024
Deep Learning for Multivariate Time Series Imputation: A Survey
Deep Learning for Multivariate Time Series Imputation: A Survey
Jun Wang
Wenjie Du
Yiyuan Yang
Linglong Qian
Wei Cao
Yuxuan Liang
Wenjia Wang
Yuxuan Liang
Qingsong Wen
AI4TSSyDaBDL
92
44
0
06 Feb 2024
Handling Missing Data with Graph Representation Learning
Handling Missing Data with Graph Representation Learning
Jiaxuan You
Xiaobai Ma
D. Ding
Mykel Kochenderfer
J. Leskovec
63
181
0
30 Oct 2020
CASTLE: Regularization via Auxiliary Causal Graph Discovery
CASTLE: Regularization via Auxiliary Causal Graph Discovery
Trent Kyono
Yao Zhang
M. Schaar
OODCML
62
69
0
28 Sep 2020
Full Law Identification In Graphical Models Of Missing Data:
  Completeness Results
Full Law Identification In Graphical Models Of Missing Data: Completeness Results
Razieh Nabi
Rohit Bhattacharya
I. Shpitser
33
49
0
10 Apr 2020
Missing Data Imputation using Optimal Transport
Missing Data Imputation using Optimal Transport
Boris Muzellec
Julie Josse
Claire Boyer
Marco Cuturi
OT
55
124
0
10 Feb 2020
Learning Sparse Nonparametric DAGs
Learning Sparse Nonparametric DAGs
Xun Zheng
Chen Dan
Bryon Aragam
Pradeep Ravikumar
Eric Xing
CML
155
260
0
29 Sep 2019
Identification In Missing Data Models Represented By Directed Acyclic
  Graphs
Identification In Missing Data Models Represented By Directed Acyclic Graphs
Rohit Bhattacharya
Razieh Nabi
I. Shpitser
J. M. Robins
CML
44
36
0
29 Jun 2019
Causal Discovery in the Presence of Missing Data
Causal Discovery in the Presence of Missing Data
Ruibo Tu
Cheng Zhang
P. Ackermann
Bo Christer Bertilson
Clark Glymour
Hedvig Kjellström
Kun Zhang
CML
57
65
0
11 Jul 2018
Learning Dynamics of Linear Denoising Autoencoders
Learning Dynamics of Linear Denoising Autoencoders
Arnu Pretorius
Steve Kroon
Herman Kamper
AI4CE
50
26
0
14 Jun 2018
GAIN: Missing Data Imputation using Generative Adversarial Nets
GAIN: Missing Data Imputation using Generative Adversarial Nets
Jinsung Yoon
James Jordon
M. Schaar
GAN
58
1,021
0
07 Jun 2018
DAGs with NO TEARS: Continuous Optimization for Structure Learning
DAGs with NO TEARS: Continuous Optimization for Structure Learning
Xun Zheng
Bryon Aragam
Pradeep Ravikumar
Eric Xing
NoLaCMLOffRL
99
944
0
04 Mar 2018
MIDA: Multiple Imputation using Denoising Autoencoders
MIDA: Multiple Imputation using Denoising Autoencoders
Lovedeep Gondara
Ke Wang
AI4CE
83
85
0
08 May 2017
Provable Bounds for Learning Some Deep Representations
Provable Bounds for Learning Some Deep Representations
Sanjeev Arora
Aditya Bhaskara
Rong Ge
Tengyu Ma
BDL
97
335
0
23 Oct 2013
Generalized Denoising Auto-Encoders as Generative Models
Generalized Denoising Auto-Encoders as Generative Models
Yoshua Bengio
L. Yao
Guillaume Alain
Pascal Vincent
106
540
0
29 May 2013
MissForest - nonparametric missing value imputation for mixed-type data
MissForest - nonparametric missing value imputation for mixed-type data
D. Stekhoven
Peter Buhlmann
228
4,324
0
04 May 2011
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