Search Results for author:
Found 26 papers, 18 papers with code
Adapting Neural Networks for the Estimation of Treatment Effects
We propose two adaptations based on insights from the statistical literature on the estimation of treatment effects.
Ranked #2 on
Causal Inference
on IHDP
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.
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.
Using Embeddings to Correct for Unobserved Confounding in Networks
We validate the method with experiments on a semi-synthetic social network dataset.
Using Embeddings for Causal Estimation of Peer Influence in Social Networks
The main aim is to perform this adjustment nonparametrically, without functional form assumptions on either the process that generated the network or the treatment assignment and outcome processes.
The Linear Representation Hypothesis and the Geometry of Large Language Models
Using this causal inner product, we show how to unify all notions of linear representation.
Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach
Our main technical result is a generalization bound for compressed networks based on the compressed size.
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding
The purpose of this paper is to develop \emph{Austen plots}, a sensitivity analysis tool to aid such judgments by making it easier to reason about potential bias induced by unobserved confounding.
Efficient Conditionally Invariant Representation Learning
The procedure requires just a single ridge regression from $Y$ to kernelized features of $Z$, which can be done in advance.
The Holdout Randomization Test for Feature Selection in Black Box Models
We propose the holdout randomization test (HRT), an approach to feature selection using black box predictive models.
Methodology
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data
We solve this problem using recent ideas from graph sampling theory to (i) define an empirical risk for relational data and (ii) obtain stochastic gradients for this empirical risk that are automatically unbiased.
Invariant and Transportable Representations for Anti-Causal Domain Shifts
In this paper, we study representation learning under a particular notion of domain shift that both respects causal invariance and that naturally handles the "anti-causal" structure.
Invariant Representation Learning for Treatment Effect Estimation
To address this challenge, practitioners collect and adjust for the covariates, hoping that they adequately correct for confounding.
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond
A fundamental goal of scientific research is to learn about causal relationships.
Exchangeable modelling of relational data: checking sparsity, train-test splitting, and sparse exchangeable Poisson matrix factorization
A variety of machine learning tasks---e. g., matrix factorization, topic modelling, and feature allocation---can be viewed as learning the parameters of a probability distribution over bipartite graphs.
Bootstrap estimators for the tail-index and for the count statistics of graphex processes
Graphex processes resolve some pathologies in traditional random graph models, notably, providing models that are both projective and allow sparsity.
Statistics Theory Statistics Theory Primary 62F10, secondary 60G55, 60G70
Concept Algebra for (Score-Based) Text-Controlled Generative Models
This suggests these models have internal representations that encode concepts in a `disentangled' manner.
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.
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains.
Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests
We introduce counterfactual invariance as a formalization of the requirement that changing irrelevant parts of the input shouldn't change model predictions.
Counterfactual Invariance to Spurious Correlations in Text Classification
We introduce counterfactual invariance as a formalization of the requirement that changing irrelevant parts of the input shouldn't change model predictions.
The Causal Structure of Domain Invariant Supervised Representation Learning
Then, we characterize the causal structures that are compatible with this notion of invariance. With this in hand, we find conditions under which method-specific invariance notions correspond to real-world invariant structure, and we clarify the relationship between invariant structure and robustness to domain shifts.
Causal Estimation for Text Data with (Apparent) Overlap Violations
To estimate a causal effect from observational data, we need to adjust for confounding aspects of the text that affect both the treatment and outcome -- e. g., the topic or writing level of the text.
Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness
This is an intuitive standard, as reflected in the U. S. legal system, but its use is limited because counterfactuals cannot be directly observed in real-world data.
Transforming and Combining Rewards for Aligning Large Language Models
A common approach for aligning language models to human preferences is to first learn a reward model from preference data, and then use this reward model to update the language model.
On the Origins of Linear Representations in Large Language Models
To that end, we introduce a simple latent variable model to abstract and formalize the concept dynamics of the next token prediction.
