Thomas Icard

Thomas Icard

Stanford University

H-index: 22

North America-United States

About Thomas Icard

Thomas Icard, With an exceptional h-index of 22 and a recent h-index of 19 (since 2020), a distinguished researcher at Stanford University, specializes in the field of Computational Cognitive Science, Logic, Probability, Causality, Natural Language.

His recent articles reflect a diverse array of research interests and contributions to the field:

Interpretability at scale: Identifying causal mechanisms in alpaca

Comparing causal frameworks: Potential outcomes, structural models, graphs, and abstractions

A Reply to Makelov et al.(2023)'s" Interpretability Illusion" Arguments

Finding alignments between interpretable causal variables and distributed neural representations

Is causal reasoning harder than probabilistic reasoning?

The Influence of Outcome Valence on Explanation Selection in Positive Outcome Cases

The Influence of Normality on Explanation Selection in Soft Intervention Cases for Negative Outcomes

Causal abstraction for faithful model interpretation

Thomas Icard Information

University

Position

___

Citations(all)

4338

Citations(since 2020)

3873

Cited By

863

hIndex(all)

22

hIndex(since 2020)

19

i10Index(all)

45

i10Index(since 2020)

33

Email

University Profile Page

Stanford University

Google Scholar

View Google Scholar Profile

Thomas Icard Skills & Research Interests

Computational Cognitive Science

Logic

Probability

Causality

Natural Language

Top articles of Thomas Icard

Title

Journal

Author(s)

Publication Date

Interpretability at scale: Identifying causal mechanisms in alpaca

Advances in Neural Information Processing Systems

Zhengxuan Wu

Atticus Geiger

Thomas Icard

Christopher Potts

Noah Goodman

2024/2/13

Comparing causal frameworks: Potential outcomes, structural models, graphs, and abstractions

Advances in Neural Information Processing Systems

Duligur Ibeling

Thomas Icard

2024/2/13

A Reply to Makelov et al.(2023)'s" Interpretability Illusion" Arguments

arXiv preprint arXiv:2401.12631

Zhengxuan Wu

Atticus Geiger

Jing Huang

Aryaman Arora

Thomas Icard

...

2024/1/23

Finding alignments between interpretable causal variables and distributed neural representations

Atticus Geiger

Zhengxuan Wu

Christopher Potts

Thomas Icard

Noah Goodman

2024/3/15

Is causal reasoning harder than probabilistic reasoning?

The Review of Symbolic Logic

Milan Mossé

Duligur Ibeling

Thomas Icard

2024/3

The Influence of Outcome Valence on Explanation Selection in Positive Outcome Cases

Lara Kirfel

Jeong Yeon Shin

Thomas Icard

Tobias Gerstenberg

2023/2/10

The Influence of Normality on Explanation Selection in Soft Intervention Cases for Negative Outcomes

Lara Kirfel

Thomas Icard

Tobias Gerstenberg

2023/8/24

Causal abstraction for faithful model interpretation

arXiv preprint arXiv:2301.04709

Atticus Geiger

Chris Potts

Thomas Icard

2023/1/11

Causal abstraction with soft interventions

Riccardo Massidda

Atticus Geiger

Thomas Icard

Davide Bacciu

2023/8/10

On the opportunities and risks of foundation models. arXiv 2021

arXiv preprint arXiv:2108.07258

Rishi Bommasani

Drew A Hudson

Ehsan Adeli

Russ Altman

Simran Arora

...

2021/8/16

Probing the quantitative–qualitative divide in probabilistic reasoning

Annals of Pure and Applied Logic

Duligur Ibeling

Thomas Icard

Krzysztof Mierzewski

Milan Mossé

2023/7/20

A simple logic of concepts

Journal of Philosophical Logic

Thomas F Icard

Lawrence S Moss

2023/6

A semantics for causing, enabling, and preventing verbs using structural causal models

Proceedings of the Annual Meeting of the Cognitive Science Society

Angela Cao

Atticus Geiger

Elisa Kreiss

Thomas Icard

Tobias Gerstenberg

2023

Anticipating the risks and benefits of counterfactual world simulation models

Lara Kirfel

Robert J MacCoun

Thomas Icard

Tobias Gerstenberg

2023/11

Show and tell: Learning causal structures from observations and explanations

Proceedings of the Annual Meeting of the Cognitive Science Society

Andrew Nam

Christopher Hughes

Thomas Icard

Tobias Gerstenberg

2023

Estimating outcome probability in causal structures with positive and negative outcomes.

Lara Kirfel

Jeong Yeon Shin

Thomas Icard

Tobias Gerstenberg

2023/4/20

Resource rationality

Thomas F Icard

2023/9/12

On Pearl’s Hierarchy and the Foundations of Causal Inference

Elias Bareinboim

Juan D Correa

Duligur Ibeling

Thomas Icard

2022/2/28

Holistic evaluation of language models

arXiv preprint arXiv:2211.09110

Percy Liang

Rishi Bommasani

Tony Lee

Dimitris Tsipras

Dilara Soylu

...

2022/11/16

An interaction effect of norm violations on causal judgment

Cognition

Maureen Gill

Jonathan Kominsky

Thomas Icard

Joshua Knobe

2022/6/4

See List of Professors in Thomas Icard University(Stanford University)

Co-Authors

H-index: 76
Noah D. Goodman

Noah D. Goodman

Stanford University

H-index: 63
Joshua Knobe

Joshua Knobe

Yale University

H-index: 57
Fiery Cushman

Fiery Cushman

Harvard University

H-index: 38
Elias Bareinboim

Elias Bareinboim

Columbia University in the City of New York

H-index: 32
Lawrence S. Moss

Lawrence S. Moss

Indiana University Bloomington

H-index: 28
Tobias Gerstenberg

Tobias Gerstenberg

Stanford University

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