Publications

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2021

How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference Inproceedings

Amanda M Gentzel, Purva Pruthi, David Jensen

International Conference on Machine Learning, pp. 3660–3671, PMLR 2021.

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Improving Causal Inference by Increasing Model Expressiveness Inproceedings

David Jensen

Proceedings of the AAAI Conference on Artificial Intelligence, pp. 15053–15057, 2021.

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Preserving Privacy in Personalized Models for Distributed Mobile Services Miscellaneous

Akanksha Atrey, Prashant J Shenoy, David Jensen

2021.

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A Simulation-Based Test of Identifiability for Bayesian Causal Inference Miscellaneous

Sam Witty, David Jensen, Vikash Mansinghka

2021.

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2020

Object conditioning for causal inference Inproceedings

David Jensen, Javier Burroni, Matthew Rattigan

Uncertainty in Artificial Intelligence, pp. 1072–1082, PMLR 2020.

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Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning Inproceedings

Akanksha Atrey, Kaleigh Clary, David Jensen

International Conference on Learning Representations, 2020.

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Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates Inproceedings

Katherine A Keith, David Jensen, Brendan O'Connor

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pp. 5332–5344, Association for Computational Linguistics, 2020.

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Causal Inference using Gaussian Processes with Structured Latent Confounders Inproceedings

Sam Witty, Kenta Takatsu, David Jensen, Vikash Mansinghka

Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, pp. 10313–10323, PMLR, 2020.

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Using Experimental Data to Evaluate Methods for Observational Causal Inference Miscellaneous

Amanda Gentzel, Justin Clarke, David Jensen

2020.

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2019

PlanAlyzer: assessing threats to the validity of online experiments Journal Article

Emma Tosch, Eytan Bakshy, Emery D Berger, David Jensen, Eliot J B Moss

Proceedings of the ACM on Programming Languages, 3 (OOPSLA), pp. 1–30, 2019.

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Comment: Strengthening empirical evaluation of causal inference methods Journal Article

David Jensen, others

Statistical Science, 34 (1), pp. 77–81, 2019.

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Identifying when effect restoration will improve estimates of causal effect Inproceedings

Huseyin Oktay, Akanksha Atrey, David Jensen

Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 190–198, Society for Industrial and Applied Mathematics 2019.

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The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data Inproceedings

Amanda Gentzel, Dan Garant, David Jensen

Advances in Neural Information Processing Systems, Curran Associates, Inc., 2019.

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Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning Miscellaneous

Emma Tosch, Kaleigh Clary, John Foley, David Jensen

2019.

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Bayesian causal inference via probabilistic program synthesis Miscellaneous

Sam Witty, Alexander Lew, David Jensen, Vikash Mansinghka

2019.

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2018

Toybox: Better Atari Environments for Testing Reinforcement Learning Agents Inproceedings

John Foley, Emma Tosch, Kaleigh Clary, David Jensen

NeurIPS 2018 Workshop on Systems for ML, 2018.

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Causal Graphs vs. Causal Programs: The Case of Conditional Branching Inproceedings

Sam Witty, David Jensen

First Conference on Probabilistic Programming (ProbProg), 2018.

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Measuring and characterizing generalization in deep reinforcement learning Miscellaneous

Sam Witty, Jun Ki Lee, Emma Tosch, Akanksha Atrey, Michael Littman, David Jensen

2018.

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Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments Miscellaneous

Kaleigh Clary, Emma Tosch, John Foley, David Jensen

2018.

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2017

A/B Testing in Networks with Adversarial Members Journal Article

Kaleigh Clary, David Jensen

2017.

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INTERACTIVE WRITING AND DEBUGGING OF BAYESIAN PROBABILISTIC PROGRAMS Journal Article

Javier Burroni, Arjun Guha, David Jensen

2017.

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A/B Testing in Networks with Adversarial Nodes Inproceedings

Kaleigh Clary, Andrew McGregor, David Jensen

KDD Workshop on Mining and Learning with Graphs, 2017.

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On causal analysis for heterogeneous networks Inproceedings

Katerina Marazopoulou, David Arbour, David Jensen

The 2017 ACM SIGKDD Workshop on Causal Discovery, 2017.

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2016

Inferring Network Effects from Observational Data Inproceedings

David Arbour, Dan Garant, David Jensen

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016, pp. 715–724, ACM, 2016.

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Inferring Causal Direction from Relational Data Inproceedings

David Arbour, Katerina Marazopoulou, David Jensen

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, UAI 2016, June 25-29, 2016, New York City, NY, USA, AUAI Press, 2016.

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Controversy Detection in Wikipedia Using Collective Classification Inproceedings

Shiri Dori-Hacohen, David Jensen, James Allan

Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, July 17-21, 2016, pp. 797–800, ACM, 2016.

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Evaluating causal models by comparing interventional distributions Miscellaneous

Dan Garant, David Jensen

2016.

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Causal Discovery for Manufacturing Domains Miscellaneous

Katerina Marazopoulou, Rumi Ghosh, Prasanth Lade, David Jensen

2016.

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2015

Learning to Uncover Deep Musical Structure Inproceedings

Phillip B Kirlin, David Jensen

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, USA, pp. 1770–1776, AAAI Press, 2015.

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Teaching Computing as Science in a Research Experience Inproceedings

Jerod J Weinman, David Jensen, David Lopatto

Proceedings of the 46th ACM Technical Symposium on Computer Science Education, SIGCSE 2015, Kansas City, MO, USA, March 4-7, 2015, pp. 24–29, ACM, 2015.

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Learning the Structure of Causal Models with Relational and Temporal Dependence Inproceedings

Katerina Marazopoulou, Marc Maier, David Jensen

Proceedings of the UAI 2015 Workshop on Advances in Causal Inference co-located with the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), Amsterdam, The Netherlands, July 16, 2015, pp. 66–75, CEUR-WS.org, 2015.

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2014

Classifier-adjusted density estimation for anomaly detection and one-class classification Inproceedings

Lisa Friedland, Amanda Gentzel, David Jensen

Proceedings of the 2014 SIAM International Conference on Data Mining, pp. 578–586, Society for Industrial and Applied Mathematics 2014.

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Propensity Score Matching for Causal Inference with Relational Data Inproceedings

David Arbour, Katerina Marazopoulou, Dan Garant, David Jensen

Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction co-located with 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), Quebec City, Canada, July 27, 2014, pp. 25–34, CEUR-WS.org, 2014.

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Refining the Semantics of Social Influence Miscellaneous

Katerina Marazopoulou, David Arbour, David Jensen

2014.

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2013

Agglomerative Clustering of Bagged Data Using Joint Distributions Journal Article

David Arbour, James Atwood, Ahmed El-Kishky, David Jensen

2013.

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Copy or Coincidence? A Model for Detecting Social Influence and Duplication Events Inproceedings

Lisa Friedland, David Jensen, Michael Lavine

Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16-21 June 2013, pp. 1175–1183, JMLR.org, 2013.

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A Sound and Complete Algorithm for Learning Causal Models from Relational Data Inproceedings

Marc Maier, Katerina Marazopoulou, David Arbour, David Jensen

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, UAI 2013, Bellevue, WA, USA, August 11-15, 2013, AUAI Press, 2013.

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Flattening network data for causal discovery: What could go wrong? Inproceedings

Marc Maier, Katerina Marazopoulou, David Arbour, David Jensen

Workshop on Information in Networks, 2013.

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Reasoning about Independence in Probabilistic Models of Relational Data Miscellaneous

Marc Maier, Katerina Marazopoulou, David Jensen

2013.

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2012

Leveraging Relational Representations for Causal Discovery PhD Thesis

Matthew Rattigan

2012, ISBN: 9781267786821, (AAI3545976).

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2011

Indexing Network Structure with Shortest-Path Trees Journal Article

Marc Maier, Matthew Rattigan, David Jensen

ACM Trans. Knowl. Discov. Data, 5 (3), 2011, ISSN: 1556-4681.

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Relational blocking for causal discovery Inproceedings

Matthew Rattigan, Marc Maier, David Jensen

Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011.

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Probabilistic Modeling of Hierarchical Music Analysis. Inproceedings

Phillip B Kirlin, David Jensen

Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR, pp. 393–398, 2011.

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Distance estimation for very large networks using mapreduce and network structure indices Inproceedings

Huseyin Oktay, Soner A Balkir, Ian Foster, David Jensen

Workshop on Information Networks, 2011.

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2010

Resisting structural re-identification in anonymized social networks Journal Article

Michael Hay, Gerome Miklau, David Jensen, Don Towsley, Chao Li

The VLDB Journal, 19 (6), pp. 797–823, 2010.

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Analyzing private network data Journal Article

Michael Hay, Gerome Miklau, David Jensen

Privacy-aware knowledge discovery: Novel applications and new techniques, pp. 459–498, 2010.

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Learning causal models of relational domains Inproceedings

Marc Maier, Brian Taylor, Huseyin Oktay, David Jensen

Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, Atlanta, Georgia, USA, July 11-15, 2010, 2010.

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Causal discovery in social media using quasi-experimental designs Inproceedings

Huseyin Oktay, Brian Taylor, David Jensen

Proceedings of the 3rd Workshop on Social Network Mining and Analysis, SNAKDD, pp. 1–9, 2010.

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The application of statistical relational learning to a database of criminal and terrorist activity Inproceedings

Brian Delaney, Andrew Fast, W Campbell, C Weinstein, David Jensen

Proceedings of the 2010 SIAM International Conference on Data Mining, pp. 409–417, Society for Industrial and Applied Mathematics 2010.

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Leveraging d-separation for relational data sets Inproceedings

Matthew Rattigan, David Jensen

ICDM 2010, The 10th IEEE International Conference on Data Mining, pp. 989–994, IEEE 2010.

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79 entries « 1 of 2 »