Search Results for author:
Found 15 papers, 6 papers with code
Evaluating Interventional Reasoning Capabilities of Large Language Models
Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system.
In-Context Learning Can Re-learn Forbidden Tasks
Specifically, we investigate whether in-context learning (ICL) can be used to re-learn forbidden tasks despite the explicit fine-tuning of the model to refuse them.
Estimating Social Influence from Observational Data
We consider the problem of estimating social influence, the effect that a person's behavior has on the future behavior of their peers.
Identifiable Deep Generative Models via Sparse Decoding
The underlying model is sparse in that each observed feature (i. e. each dimension of the data) depends on a small subset of the latent factors.
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond
A fundamental goal of scientific research is to learn about causal relationships.
Causal Effects of Linguistic Properties
Second, in practice, we only have access to noisy proxies for the linguistic properties of interest -- e. g., predictions from classifiers and lexicons.
Valid Causal Inference with (Some) Invalid Instruments
The technique is simple to apply and is "black-box" in the sense that it may be used with any instrumental variable estimator as long as the treatment effect is identified for each valid instrument independently.
Estimating Causal Effects of Tone in Online Debates
In this paper, we estimate the causal effect of reply tones in debates on linguistic and sentiment changes in subsequent responses.
Adapting Text Embeddings for Causal Inference
To address this challenge, we develop causally sufficient embeddings, low-dimensional document representations that preserve sufficient information for causal identification and allow for efficient estimation of causal effects.
Equal Opportunity and Affirmative Action via Counterfactual Predictions
Machine learning (ML) can automate decision-making by learning to predict decisions from historical data.
Scalable Structure Learning for Probabilistic Soft Logic
We introduce a greedy search-based algorithm and a novel optimization method that trade-off scalability and approximations to the structure learning problem in varying ways.
Using Noisy Extractions to Discover Causal Knowledge
Knowledge bases (KB) constructed through information extraction from text play an important role in query answering and reasoning.
Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks
A neighborhood graph, which represents the instances as vertices and their relations as weighted edges, is the basis of many semi-supervised and relational models for node labeling and link prediction.
