Dhanya Sridhar

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University of California Santa Cruz ∙ Columbia University

Causal Effects of Linguistic Properties

We consider the problem of estimating the causal effects of linguistic p...
10/24/2020 ∙ by Reid Pryzant, et al. ∙ 0 ∙ share

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Valid Causal Inference with (Some) Invalid Instruments

Instrumental variable methods provide a powerful approach to estimating ...
06/19/2020 ∙ by Jason Hartford, et al. ∙ 8 ∙ share

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Estimating Causal Effects of Tone in Online Debates

Statistical methods applied to social media posts shed light on the dyna...
06/10/2019 ∙ by Dhanya Sridhar, et al. ∙ 0 ∙ share

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Using Text Embeddings for Causal Inference

We address causal inference with text documents. For example, does addin...
05/29/2019 ∙ by Victor Veitch, et al. ∙ 0 ∙ share

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Equal Opportunity and Affirmative Action via Counterfactual Predictions

Machine learning (ML) can automate decision-making by learning to predic...
05/26/2019 ∙ by Yixin Wang, et al. ∙ 0 ∙ share

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Scalable Structure Learning for Probabilistic Soft Logic

Statistical relational frameworks such as Markov logic networks and prob...
07/03/2018 ∙ by Varun Embar, et al. ∙ 4 ∙ share

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Using Noisy Extractions to Discover Causal Knowledge

Knowledge bases (KB) constructed through information extraction from tex...
11/16/2017 ∙ by Dhanya Sridhar, et al. ∙ 0 ∙ share

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Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks

A neighborhood graph, which represents the instances as vertices and the...
07/02/2016 ∙ by Shobeir Fakhraei, et al. ∙ 0 ∙ share

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Dhanya Sridhar

Featured Co-authors

David M. Blei

Dan Jurafsky

Yixin Wang

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Kevin Leyton-Brown

Victor Veitch

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Golnoosh Farnadi

Jay Pujara

Jason Hartford

Shobeir Fakhraei

Causal Effects of Linguistic Properties

Valid Causal Inference with (Some) Invalid Instruments

Estimating Causal Effects of Tone in Online Debates

Using Text Embeddings for Causal Inference

Equal Opportunity and Affirmative Action via Counterfactual Predictions

Scalable Structure Learning for Probabilistic Soft Logic

Using Noisy Extractions to Discover Causal Knowledge

Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks