93 citations · 364 across the 27 of their papers we have counts for
28 papers
Food Choice Mimicry on a Large University Campus
Kristina Gligoric, Arnaud Chiolero, Emre Kıcıman +3
Social influence is a strong determinant of food consumption, which in turn influences health. Although consistent observations have been made on the role of social factors in driv…
Knowledge Guided Representation Learning and Causal Structure Learning in Soil Science
Somya Sharma, Swati Sharma, Licheng Liu +6
An improved understanding of soil can enable more sustainable land-use practices. Nevertheless, soil is called a complex, living medium due to the complex interaction of different…
A Causal Roadmap for Generating High-Quality Real-World Evidence
Lauren E Dang, Susan Gruber, Hana Lee +23
Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks…
Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
Emre Kıcıman, Robert Ness, Amit Sharma +1
The causal capabilities of large language models (LLMs) are a matter of significant debate, with critical implications for the use of LLMs in societally impactful domains such as m…
Counterfactual (Non-)identifiability of Learned Structural Causal Models
Arash Nasr-Esfahany, Emre Kiciman
Recent advances in probabilistic generative modeling have motivated learning Structural Causal Models (SCM) from observational datasets using deep conditional generative models, al…
Causal Modeling of Soil Processes for Improved Generalization
Somya Sharma, Swati Sharma, Andy Neal +5
Measuring and monitoring soil organic carbon is critical for agricultural productivity and for addressing critical environmental problems. Soil organic carbon not only enriches nut…
Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systems
Parikshit Bansal, Yashoteja Prabhu, Emre Kiciman +1
Given a user's input text, text-matching recommender systems output relevant items by comparing the input text to available items' description, such as product-to-product recommend…
Language Model Decoding as Likelihood-Utility Alignment
Martin Josifoski, Maxime Peyrard, Frano Rajic +8
A critical component of a successful language generation pipeline is the decoding algorithm. However, the general principles that should guide the choice of a decoding algorithm re…
Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization
Jivat Neet Kaur, Emre Kiciman, Amit Sharma
Recent empirical studies on domain generalization (DG) have shown that DG algorithms that perform well on some distribution shifts fail on others, and no state-of-the-art DG algori…
Investigations of Performance and Bias in Human-AI Teamwork in Hiring
Andi Peng, Besmira Nushi, Emre Kiciman +2
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. While prior w…
Deep End-to-end Causal Inference
Tomas Geffner, Javier Antoran, Adam Foster +9
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making. However, research on causal discovery…
Invariant Language Modeling
Maxime Peyrard, Sarvjeet Singh Ghotra, Martin Josifoski +5
Large pretrained language models are critical components of modern NLP pipelines. Yet, they suffer from spurious correlations, poor out-of-domain generalization, and biases. Inspir…
Population-scale dietary interests during the COVID-19 pandemic
Kristina Gligoric, Arnaud Chiolero, Emre Kıcıman +2
The SARS-CoV-2 virus has altered people's lives around the world. Here we document population-wide shifts in dietary interests in 18 countries in 2020, as revealed through time ser…
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
Amit Sharma, Vasilis Syrgkanis, Cheng Zhang +1
Estimation of causal effects involves crucial assumptions about the data-generating process, such as directionality of effect, presence of instrumental variables or mediators, and…
Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fix
Ruocheng Guo, Pengchuan Zhang, Hao Liu +1
This work considers the out-of-distribution (OOD) prediction problem where (1)~the training data are from multiple domains and (2)~the test domain is unseen in the training. DNNs f…
Formation of Social Ties Influences Food Choice: A Campus-Wide Longitudinal Study
Kristina Gligorić, Ryen W. White, Emre Kıcıman +3
Nutrition is a key determinant of long-term health, and social influence has long been theorized to be a key determinant of nutrition. It has been difficult to quantify the postula…
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions
Yanbo Xu, Divyat Mahajan, Liz Manrao +2
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 maximizin…
DoWhy: An End-to-End Library for Causal Inference
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…
Causal Inference in the Presence of Interference in Sponsored Search Advertising
Razieh Nabi, Joel Pfeiffer, Murat Ali Bayir +2
In classical causal inference, inferring cause-effect relations from data relies on the assumption that units are independent and identically distributed. This assumption is violat…
Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction
Shuxi Zeng, Murat Ali Bayir, Joesph J. Pfeiffer +2
It is often critical for prediction models to be robust to distributional shifts between training and testing data. From a causal perspective, the challenge is to distinguish the s…
Security and Machine Learning in the Real World
Ivan Evtimov, Weidong Cui, Ece Kamar +3
Machine learning (ML) models deployed in many safety- and business-critical systems are vulnerable to exploitation through adversarial examples. A large body of academic research h…
AvE: Assistance via Empowerment
Yuqing Du, Stas Tiomkin, Emre Kiciman +3
One difficulty in using artificial agents for human-assistive applications lies in the challenge of accurately assisting with a person's goal(s). Existing methods tend to rely on i…
Novel Human-Object Interaction Detection via Adversarial Domain Generalization
Yuhang Song, Wenbo Li, Lei Zhang +6
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. The challenge…
What You See Is What You Get? The Impact of Representation Criteria on Human Bias in Hiring
Andi Peng, Besmira Nushi, Emre Kiciman +3
Although systematic biases in decision-making are widely documented, the ways in which they emerge from different sources is less understood. We present a controlled experimental p…