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Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition

9 July 2017

Papers citing "Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition"

50 / 113 papers shown

Title
Stochastic Tree Ensembles for Estimating Heterogeneous Effects Nikolay M. Krantsevich Jingyu He P. R. Hahn CML 85 13 0 15 Sep 2022
Normalizing Flows for Interventional Density Estimation Valentyn Melnychuk Dennis Frauen Stefan Feuerriegel 101 21 0 13 Sep 2022
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability Jonathan Crabbé Alicia Curth Ioana Bica M. Schaar CML 110 16 0 16 Jun 2022
Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes Leon Yao Caroline Lo Israel Nir S. Tan Ariel Evnine Adam Lerer A. Peysakhovich CML 57 7 0 10 Jun 2022
Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects Naoufal Acharki Ramiro Lugo A. Bertoncello Josselin Garnier CML 70 13 0 29 May 2022
Extraction of Visual Information to Predict Crowdfunding Success Simon J. Blanchard Theodore J. Noseworthy E. Pancer Maxwell Poole 33 10 0 28 Mar 2022
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments Kosuke Imai Michael Lingzhi Li CML 39 15 0 28 Mar 2022
Neural Score Matching for High-Dimensional Causal Inference Oscar Clivio Fabian Falck B. Lehmann George Deligiannidis Chris Holmes CML 66 8 0 01 Mar 2022
Estimating causal effects with optimization-based methods: A review and empirical comparison Martin Cousineau V. Verter Susan Murphy J. Pineau CML 40 9 0 28 Feb 2022
Ensemble Method for Estimating Individualized Treatment Effects K. Han Hanghao Wu CML FedML 29 4 0 25 Feb 2022
Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy A. Linero 79 10 0 20 Feb 2022
A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling Eustache Diemert Artem Betlei Christophe Renaudin Massih-Reza Amini T. Gregoir Thibaud Rahier CML 71 10 0 19 Nov 2021
ADCB: An Alzheimer's disease benchmark for evaluating observational estimators of causal effects N. M. Kinyanjui Fredrik D. Johansson CML 45 0 0 12 Nov 2021
Positivity Validation Detection and Explainability via Zero Fraction Multi-Hypothesis Testing and Asymmetrically Pruned Decision Trees Guy Wolf G. Shabat H. Shteingart 72 1 0 07 Nov 2021
Improved inference for doubly robust estimators of heterogeneous treatment effects Hee-Choon Shin Joseph Antonelli 56 4 0 05 Nov 2021
GeneDisco: A Benchmark for Experimental Design in Drug Discovery Arash Mehrjou Ashkan Soleymani Andrew Jesson Pascal Notin Y. Gal Stefan Bauer Patrick Schwab 89 21 0 22 Oct 2021
Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Community M. Glenski Svitlana Volkova CML AI4CE 84 1 0 15 Oct 2021
A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patients A. Izdebski P. Thoral R. Lalisang Dean McHugh D. Gommers ... Rutger van Raalte M. V. Tellingen Niels C. Gritters van den Oever Paul Elbers Giovanni Cina CML 48 0 0 14 Sep 2021
Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators Alicia Curth M. Schaar CML 49 7 0 28 Jul 2021
On Inductive Biases for Heterogeneous Treatment Effect Estimation Alicia Curth M. Schaar CML 193 84 0 07 Jun 2021
Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters Carlos Fernández-Loría F. Provost CML 67 45 0 08 Apr 2021
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge Trent Kyono Ioana Bica Zhaozhi Qian Mihaela van der Schaar OOD CML 28 7 0 11 Feb 2021
Generating Synthetic Text Data to Evaluate Causal Inference Methods Zach Wood-Doughty I. Shpitser Mark Dredze SyDa CML 68 11 0 10 Feb 2021
RealCause: Realistic Causal Inference Benchmarking Brady Neal Chin-Wei Huang Sunand Raghupathi CML ELM 72 34 0 30 Nov 2020
Counterfactual Representation Learning with Balancing Weights Serge Assaad Shuxi Zeng Chenyang Tao Shounak Datta Nikhil Mehta Ricardo Henao Fan Li Lawrence Carin CML OOD 178 65 0 23 Oct 2020
How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference A. Gentzel Purva Pruthi David D. Jensen CML 67 18 0 06 Oct 2020
Probabilistic Machine Learning for Healthcare Irene Y. Chen Shalmali Joshi Marzyeh Ghassemi Rajesh Ranganath OOD 69 56 0 23 Sep 2020
Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference Galen Cassebeer Weld Peter West M. Glenski David Arbour Ryan Rossi Tim Althoff CML 102 20 0 21 Sep 2020
Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects Falco J. Bargagli-Stoffi Riccardo Cadei Kwonsang Lee Francesca Dominici CML 58 16 0 18 Sep 2020
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review A. Caron G. Baio I. Manolopoulou CML 94 56 0 14 Sep 2020
Estimating heterogeneous survival treatment effect in observational data using machine learning Liangyuan Hu Jiayi Ji Fan Li CML 94 67 0 17 Aug 2020
A unified survey of treatment effect heterogeneity modeling and uplift modeling Weijia Zhang Jiuyong Li Lin Liu CML 97 60 0 14 Jul 2020
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models Andrew Jesson Sören Mindermann Uri Shalit Y. Gal CML 76 74 0 01 Jul 2020
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification Hyun-Suk Lee Yao Zhang W. Zame Cong Shen Jang-Won Lee M. Schaar CML 41 19 0 14 Jun 2020
Conformal Inference of Counterfactuals and Individual Treatment Effects Lihua Lei Emmanuel J. Candès CML 174 195 0 11 Jun 2020
Bayesian Probabilistic Numerical Integration with Tree-Based Models Harrison Zhu Xing Liu Ruya Kang Zhichao Shen Seth Flaxman F. Briol TPM 42 5 0 09 Jun 2020
CausaLM: Causal Model Explanation Through Counterfactual Language Models Amir Feder Nadav Oved Uri Shalit Roi Reichart CML LRM 161 162 0 27 May 2020
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates Katherine A. Keith David D. Jensen Brendan O'Connor CML 68 114 0 01 May 2020
A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation Carlos Fernández-Loría F. Provost J. Anderton Benjamin Carterette Praveen Chandar CML 61 19 0 24 Apr 2020
Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding Victor Veitch A. Zaveri CML 170 52 0 03 Mar 2020
A Survey on Causal Inference Liuyi Yao Zhixuan Chu Sheng Li Yaliang Li Jing Gao Aidong Zhang CML 117 516 0 05 Feb 2020
Treatment effect estimation with disentangled latent factors Weijia Zhang Lin Liu Jiuyong Li CML 99 89 0 29 Jan 2020
Response Transformation and Profit Decomposition for Revenue Uplift Modeling R. M. Gubela Stefan Lessmann S. Jaroszewicz OffRL 62 51 0 20 Nov 2019
The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data A. Gentzel Dan Garant David D. Jensen CML ELM 61 47 0 11 Oct 2019
Estimation of Bounds on Potential Outcomes For Decision Making Maggie Makar Fredrik D. Johansson John Guttag David Sontag 27 1 0 10 Oct 2019
Linear Aggregation in Tree-based Estimators Sören R. Künzel Theo Saarinen Edward W. Liu Jasjeet Sekhon 141 10 0 15 Jun 2019
The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis Cynthia Rudin David Carlson HAI 122 34 0 04 Jun 2019
An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference Y. Shimoni Ehud Karavani Sivan Ravid Peter Bak Tan Hung Marie Ng S. Alford D. Meade Yaara Goldschmidt ELM CML 73 38 0 02 Jun 2019
Heterogeneous causal effects with imperfect compliance: a Bayesian machine learning approach Falco J. Bargagli-Stoffi Kristof De-Witte G. Gnecco 90 15 0 29 May 2019
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data Xin Du Lei Sun W. Duivesteijn Alexander G. Nikolaev Mykola Pechenizkiy OOD CML 79 44 0 30 Apr 2019