activity
20192021
most citedAdapting Text Embeddings for Causal Inference

66 citations · 99 across the 6 of their papers we have counts for

collaborators

7 papers

stat.ME2021

On the Assumptions of Synthetic Control Methods

Claudia Shi, Dhanya Sridhar, Vishal Misra +1

Synthetic control (SC) methods have been widely applied to estimate the causal effect of large-scale interventions, e.g., the state-wide effect of a change in policy. The idea of s…

stat.ML2021★ 2 cited

Identifiable Deep Generative Models via Sparse Decoding

Gemma E. Moran, Dhanya Sridhar, Yixin Wang +1

We develop the sparse VAE for unsupervised representation learning on high-dimensional data. The sparse VAE learns a set of latent factors (representations) which summarize the ass…

cs.CL2021★ 15 cited

Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond

Amir Feder, Katherine A. Keith, Emaad Manzoor +10

A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same…

cs.CL2020

Causal Effects of Linguistic Properties

Reid Pryzant, Dallas Card, Dan Jurafsky +2

We consider the problem of using observational data to estimate the causal effects of linguistic properties. For example, does writing a complaint politely lead to a faster respons…

stat.ME2020★ 6 cited

Valid Causal Inference with (Some) Invalid Instruments

Jason Hartford, Victor Veitch, Dhanya Sridhar +1

Instrumental variable methods provide a powerful approach to estimating causal effects in the presence of unobserved confounding. But a key challenge when applying them is the reli…

stat.ML2019★ 10 cited

Equal Opportunity and Affirmative Action via Counterfactual Predictions

Yixin Wang, Dhanya Sridhar, David M. Blei

Machine learning (ML) can automate decision-making by learning to predict decisions from historical data. However, these predictors may inherit discriminatory policies from past de…

cs.LG2019★ 66 cited

Adapting Text Embeddings for Causal Inference

Victor Veitch, Dhanya Sridhar, David M. Blei

Does adding a theorem to a paper affect its chance of acceptance? Does labeling a post with the author's gender affect the post popularity? This paper develops a method to estimate…