ncsulsj / Causal_LLM
☆14Updated 7 months ago
Related projects: ⓘ
- Solving the causality pairs challenge (does A cause B) with ChatGPT☆73Updated 3 months ago
- Code for paper: Are Large Language Models Post Hoc Explainers?☆21Updated last month
- NLPBench: Evaluating NLP-Related Problem-solving Ability in Large Language Models☆9Updated 10 months ago
- Deep Counterfactual Prediction with Categorical Backward Variables☆12Updated last year
- Efficient Full Causal Graph Discovery Using Large Language Models☆21Updated 7 months ago
- Lightweight Adapting for Black-Box Large Language Models☆16Updated 7 months ago
- We develop benchmarks and analysis tools to evaluate the causal reasoning abilities of LLMs.☆87Updated 3 months ago
- Data and code for the Corr2Cause paper (ICLR 2024)☆79Updated 5 months ago
- ☆11Updated 7 months ago
- ☆18Updated 3 years ago
- Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.☆37Updated 5 months ago
- [ NeurIPS 2023 ] Official Codebase for "Conformal Meta-learners for Predictive Inference of Individual Treatment Effects"☆41Updated 11 months ago
- Uses several statistical tests / algorithms on marginal / conditional distributions☆8Updated last year
- Testing Language Models for Memorization of Tabular Datasets.☆26Updated last week
- Extending Conformal Prediction to LLMs☆53Updated 2 months ago
- Bayesian low-rank adaptation for large language models☆22Updated 4 months ago
- Causal Modeling with Stationary Diffusions, AISTATS 2024☆12Updated 2 months ago
- ☆30Updated last year
- Repository of paper "LLMs with Chain-of-Thought Are Non-Causal Reasoners"☆14Updated 5 months ago
- Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations (ICML 2022)☆23Updated 2 years ago
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)☆38Updated last year
- For calculating Shapley values via linear regression.☆61Updated 3 years ago
- ☆11Updated last year
- Official implementation of the paper "Interventions, Where and How? Experimental Design for Causal Models at Scale", NeurIPS 2022.☆19Updated last year
- Conformal Language Modeling☆20Updated 8 months ago
- Code for the ICLR 2021 Paper "In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness"☆12Updated 2 years ago
- ☆11Updated 2 years ago
- Interpretable text embeddings by asking LLMs yes/no questions☆17Updated 3 months ago
- ☆11Updated last year
- Experimental library integrating LLM capabilities to support causal analyses☆70Updated last week