Search Results for author: Emre Kiciman

Found 23 papers, 6 papers with code

Causal Reasoning and Large Language Models: Opening a New Frontier for Causality

no code implementations28 Apr 2023 Emre Kiciman, Robert Ness, Amit Sharma, Chenhao Tan

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.

Causal Discovery Common Sense Reasoning

Counterfactual (Non-)identifiability of Learned Structural Causal Models

no code implementations22 Jan 2023 Arash Nasr-Esfahany, Emre Kiciman

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.

Counterfactual Inference

Causal Modeling of Soil Processes for Improved Generalization

no code implementations10 Nov 2022 Somya Sharma, Swati Sharma, Andy Neal, Sara Malvar, Eduardo Rodrigues, John Crawford, Emre Kiciman, Ranveer Chandra

Measuring and monitoring soil organic carbon is critical for agricultural productivity and for addressing critical environmental problems.

Causal Discovery Management +1

Language Model Decoding as Likelihood-Utility Alignment

1 code implementation13 Oct 2022 Martin Josifoski, Maxime Peyrard, Frano Rajic, Jiheng Wei, Debjit Paul, Valentin Hartmann, Barun Patra, Vishrav Chaudhary, Emre Kiciman, Boi Faltings, Robert West

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.

Language Modelling Text Generation

Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systems

no code implementations7 Oct 2022 Parikshit Bansal, Yashoteja Prabhu, Emre Kiciman, Amit Sharma

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.

Language Modelling Out-of-Distribution Generalization +3

Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization

no code implementations15 Jun 2022 Jivat Neet Kaur, Emre Kiciman, Amit Sharma

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.

Domain Generalization Out-of-Distribution Generalization

Investigations of Performance and Bias in Human-AI Teamwork in Hiring

no code implementations21 Feb 2022 Andi Peng, Besmira Nushi, Emre Kiciman, Kori Inkpen, Ece Kamar

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.

Decision Making

Deep End-to-end Causal Inference

1 code implementation4 Feb 2022 Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang

Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making.

Causal Discovery Causal Inference +1

Invariant Language Modeling

1 code implementation16 Oct 2021 Maxime Peyrard, Sarvjeet Singh Ghotra, Martin Josifoski, Vidhan Agarwal, Barun Patra, Dean Carignan, Emre Kiciman, Robert West

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.

Domain Generalization Language Modelling

FCause: Flow-based Causal Discovery

no code implementations29 Sep 2021 Tomas Geffner, Emre Kiciman, Angus Lamb, Martin Kukla, Miltiadis Allamanis, Cheng Zhang

Current causal discovery methods either fail to scale, model only limited forms of functional relationships, or cannot handle missing values.

Causal Discovery

DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions

1 code implementation27 Aug 2021 Amit Sharma, Vasilis Syrgkanis, Cheng Zhang, Emre Kiciman

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.

Causal Discovery

Formation of Social Ties Influences Food Choice: A Campus-Wide Longitudinal Study

no code implementations17 Feb 2021 Kristina Gligorić, Ryen W. White, Emre Kiciman, Eric Horvitz, Arnaud Chiolero, Robert West

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.

Experimental Design Nutrition

Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fix

no code implementations16 Jan 2021 Ruocheng Guo, Pengchuan Zhang, Hao liu, Emre Kiciman

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.

Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions

no code implementations11 Nov 2020 Yanbo Xu, Divyat Mahajan, Liz Manrao, Amit Sharma, Emre Kiciman

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.

Causal Inference Marketing

DoWhy: An End-to-End Library for Causal Inference

4 code implementations9 Nov 2020 Amit Sharma, Emre Kiciman

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.

Causal Inference

Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction

no code implementations17 Oct 2020 Shuxi Zeng, Murat Ali Bayir, Joesph J. Pfeiffer III, Denis Charles, Emre Kiciman

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.

Security and Machine Learning in the Real World

no code implementations13 Jul 2020 Ivan Evtimov, Weidong Cui, Ece Kamar, Emre Kiciman, Tadayoshi Kohno, Jerry Li

Machine learning (ML) models deployed in many safety- and business-critical systems are vulnerable to exploitation through adversarial examples.

BIG-bench Machine Learning

AvE: Assistance via Empowerment

1 code implementation NeurIPS 2020 Yuqing Du, Stas Tiomkin, Emre Kiciman, Daniel Polani, Pieter Abbeel, Anca Dragan

One difficulty in using artificial agents for human-assistive applications lies in the challenge of accurately assisting with a person's goal(s).

Novel Human-Object Interaction Detection via Adversarial Domain Generalization

no code implementations22 May 2020 Yuhang Song, Wenbo Li, Lei Zhang, Jianwei Yang, Emre Kiciman, Hamid Palangi, Jianfeng Gao, C. -C. Jay Kuo, Pengchuan Zhang

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.

Domain Generalization Human-Object Interaction Detection

What You See Is What You Get? The Impact of Representation Criteria on Human Bias in Hiring

no code implementations8 Sep 2019 Andi Peng, Besmira Nushi, Emre Kiciman, Kori Inkpen, Siddharth Suri, Ece Kamar

Although systematic biases in decision-making are widely documented, the ways in which they emerge from different sources is less understood.

Decision Making

Fast Variational Inference for Large-scale Internet Diagnosis

no code implementations NeurIPS 2007 Emre Kiciman, David Maltz, John C. Platt

Web servers on the Internet need to maintain high reliability, but the cause of intermittent failures of web transactions is non-obvious.

Bayesian Inference Time Series Analysis +1

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