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Found 30 papers, 8 papers with code
DoWhy: An End-to-End Library for Causal Inference
In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent.
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
Estimation of causal effects involves crucial assumptions about the data-generating process, such as directionality of effect, presence of instrumental variables or mediators, and whether all relevant confounders are observed.
Deep End-to-end Causal Inference
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making.
Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models
Based on the evaluation, our work makes a key analysis of the underlying reason for the success of the attack, namely the inability of LLMs to distinguish between instructions and external content and the absence of LLMs' awareness to not execute instructions within external content.
Language Model Decoding as Likelihood-Utility Alignment
Specifically, by analyzing the correlation between the likelihood and the utility of predictions across a diverse set of tasks, we provide empirical evidence supporting the proposed taxonomy and a set of principles to structure reasoning when choosing a decoding algorithm.
Invariant Language Modeling
In particular, we adapt a game-theoretic formulation of IRM (IRM-games) to language models, where the invariance emerges from a specific training schedule in which all the environments compete to optimize their own environment-specific loss by updating subsets of the model in a round-robin fashion.
AvE: Assistance via Empowerment
One difficulty in using artificial agents for human-assistive applications lies in the challenge of accurately assisting with a person's goal(s).
A Glitch in the Matrix? Locating and Detecting Language Model Grounding with Fakepedia
Yet the mechanisms underlying this contextual grounding remain unknown, especially in situations where contextual information contradicts factual knowledge stored in the parameters, which LLMs also excel at recalling.
Fast Variational Inference for Large-scale Internet Diagnosis
Web servers on the Internet need to maintain high reliability, but the cause of intermittent failures of web transactions is non-obvious.
What You See Is What You Get? The Impact of Representation Criteria on Human Bias in Hiring
Although systematic biases in decision-making are widely documented, the ways in which they emerge from different sources is less understood.
Novel Human-Object Interaction Detection via Adversarial Domain Generalization
We study in this paper the problem of novel human-object interaction (HOI) detection, aiming at improving the generalization ability of the model to unseen scenarios.
Security and Machine Learning in the Real World
Machine learning (ML) models deployed in many safety- and business-critical systems are vulnerable to exploitation through adversarial examples.
Causal Inference in the Presence of Interference in Sponsored Search Advertising
This assumption is violated in settings where units are related through a network of dependencies.
Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction
We describe a causal transfer random forest (CTRF) that combines existing training data with a small amount of data from a randomized experiment to train a model which is robust to the feature shifts and therefore transfers to a new targeting distribution.
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions
For many kinds of interventions, such as a new advertisement, marketing intervention, or feature recommendation, it is important to target a specific subset of people for maximizing its benefits at minimum cost or potential harm.
Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fix
Nevertheless, we find that the performance of IRM can be dramatically degraded under \emph{strong $\Lambda$ spuriousness} -- when the spurious correlation between the spurious features and the class label is strong due to the strong causal influence of their common cause, the domain label, on both of them (see Fig.
Formation of Social Ties Influences Food Choice: A Campus-Wide Longitudinal Study
To estimate causal effects from the passively observed log data, we control confounds in a matched quasi-experimental design: we identify focal users who at first do not have any regular eating partners but then start eating with a fixed partner regularly, and we match focal users into comparison pairs such that paired users are nearly identical with respect to covariates measured before acquiring the partner, where the two focal users' new eating partners diverge in the healthiness of their respective food choice.
FCause: Flow-based Causal Discovery
Current causal discovery methods either fail to scale, model only limited forms of functional relationships, or cannot handle missing values.
Investigations of Performance and Bias in Human-AI Teamwork in Hiring
In AI-assisted decision-making, effective hybrid (human-AI) teamwork is not solely dependent on AI performance alone, but also on its impact on human decision-making.
Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization
Based on the relationship between spurious attributes and the classification label, we obtain realizations of the canonical causal graph that characterize common distribution shifts and show that each shift entails different independence constraints over observed variables.
Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systems
To explain this generalization failure, we consider an intervention-based importance metric, which shows that a fine-tuned model captures spurious correlations and fails to learn the causal features that determine the relevance between any two text inputs.
Causal Modeling of Soil Processes for Improved Generalization
Measuring and monitoring soil organic carbon is critical for agricultural productivity and for addressing critical environmental problems.
Counterfactual (Non-)identifiability of Learned Structural Causal Models
The size of this error can be an essential metric for deciding whether or not DSCMs are a viable approach for counterfactual inference in a specific problem setting.
Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
The causal capabilities of large language models (LLMs) is a matter of significant debate, with critical implications for the use of LLMs in societally impactful domains such as medicine, science, law, and policy.
Knowledge Guided Representation Learning and Causal Structure Learning in Soil Science
Process-based models and analyzing observed data provide two avenues for improving our understanding of soil processes.
TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models
The rapid digitization of real-world data offers an unprecedented opportunity for optimizing healthcare delivery and accelerating biomedical discovery.
Extracting Self-Consistent Causal Insights from Users Feedback with LLMs and In-context Learning
Microsoft Windows Feedback Hub is designed to receive customer feedback on a wide variety of subjects including critical topics such as power and battery.
Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization
Using the sensor data, we preserve underlying causal relations among sensor attributes and organic matter.
Defending Against Indirect Prompt Injection Attacks With Spotlighting
Indirect prompt injection attacks take advantage of this vulnerability by embedding adversarial instructions into untrusted data being processed alongside user commands.
