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Semiparametric Inference For Causal Effects In Graphical Models With
  Hidden Variables
v1v2v3 (latest)

Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables

27 March 2020
Rohit Bhattacharya
Razieh Nabi
I. Shpitser
    CML
ArXiv (abs)PDFHTML

Papers citing "Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables"

17 / 17 papers shown
Title
New $\sqrt{n}$-consistent, numerically stable higher-order influence
  function estimators
New n\sqrt{n}n​-consistent, numerically stable higher-order influence function estimators
Lin Liu
Chang Li
57
0
0
16 Feb 2023
A Review of Off-Policy Evaluation in Reinforcement Learning
A Review of Off-Policy Evaluation in Reinforcement Learning
Masatoshi Uehara
C. Shi
Nathan Kallus
OffRL
102
76
0
13 Dec 2022
Deep Causal Learning: Representation, Discovery and Inference
Deep Causal Learning: Representation, Discovery and Inference
Zizhen Deng
Xiaolong Zheng
Hu Tian
D. Zeng
CMLBDL
119
11
0
07 Nov 2022
Finding and Listing Front-door Adjustment Sets
Finding and Listing Front-door Adjustment Sets
H. Jeong
Jin Tian
Elias Bareinboim
81
9
0
11 Oct 2022
Falsification before Extrapolation in Causal Effect Estimation
Falsification before Extrapolation in Causal Effect Estimation
Zeshan Hussain
Michael Oberst
M. Shih
David Sontag
CML
95
9
0
27 Sep 2022
Data-Driven Causal Effect Estimation Based on Graphical Causal
  Modelling: A Survey
Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey
Debo Cheng
Jiuyong Li
Lin Liu
Jixue Liu
T. Le
CML
94
32
0
20 Aug 2022
Causal Structure Learning: a Combinatorial Perspective
Causal Structure Learning: a Combinatorial Perspective
C. Squires
Caroline Uhler
CML
120
47
0
02 Jun 2022
On Testability of the Front-Door Model via Verma Constraints
On Testability of the Front-Door Model via Verma Constraints
Rohit Bhattacharya
Razieh Nabi
85
9
0
01 Mar 2022
Variable elimination, graph reduction and efficient g-formula
Variable elimination, graph reduction and efficient g-formula
F. R. Guo
Emilija Perković
A. Rotnitzky
CML
74
7
0
24 Feb 2022
A Free Lunch with Influence Functions? Improving Neural Network
  Estimates with Concepts from Semiparametric Statistics
A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics
M. Vowels
S. Akbari
Necati Cihan Camgöz
Richard Bowden
66
4
0
18 Feb 2022
Unicorn: Reasoning about Configurable System Performance through the
  lens of Causality
Unicorn: Reasoning about Configurable System Performance through the lens of Causality
Md Shahriar Iqbal
R. Krishna
Mohammad Ali Javidian
Baishakhi Ray
Pooyan Jamshidi
LRM
75
31
0
20 Jan 2022
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling
  via Simple Data Augmentation
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation
Takeshi Teshima
Masashi Sugiyama
CML
91
13
0
27 Feb 2021
Differentiable Causal Discovery Under Unmeasured Confounding
Differentiable Causal Discovery Under Unmeasured Confounding
Rohit Bhattacharya
Tushar Nagarajan
Daniel Malinsky
I. Shpitser
CML
80
61
0
14 Oct 2020
Path Dependent Structural Equation Models
Path Dependent Structural Equation Models
R. Srinivasan
Jaron J. R. Lee
Rohit Bhattacharya
Narges Ahmidi
I. Shpitser
CML
55
4
0
24 Aug 2020
Efficient adjustment sets in causal graphical models with hidden
  variables
Efficient adjustment sets in causal graphical models with hidden variables
Ezequiel Smucler
F. Sapienza
A. Rotnitzky
CMLOffRL
100
33
0
22 Apr 2020
Optimal Training of Fair Predictive Models
Optimal Training of Fair Predictive Models
Razieh Nabi
Daniel Malinsky
I. Shpitser
60
14
0
09 Oct 2019
Quantum Inflation: A General Approach to Quantum Causal Compatibility
Quantum Inflation: A General Approach to Quantum Causal Compatibility
Elie Wolfe
Alejandro Pozas-Kerstjens
Matan Grinberg
D. Rosset
A. Acín
M. Navascués
AI4CE
89
56
0
23 Sep 2019
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