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

11 October 2019

Papers citing "The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data"

32 / 32 papers shown

Title
Heterogeneous Causal Discovery of Repeated Undesirable Health Outcomes Shishir Adhikari Guido Muscioni Mark Shapiro Plamen Petrov Elena Zheleva CML 95 0 0 14 Mar 2025
CausalMan: A physics-based simulator for large-scale causality Nicholas Tagliapietra J. Luettin Lavdim Halilaj Moritz Willig Tim Pychynski Kristian Kersting CML 107 0 0 18 Feb 2025
Learning Personalized Treatment Decisions in Precision Medicine: Disentangling Treatment Assignment Bias in Counterfactual Outcome Prediction and Biomarker Identification Michael Vollenweider Manuel Schürch Chiara Rohrer Gabriele Gut Michael Krauthammer Andreas Wicki CML 54 0 0 01 Oct 2024
Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual Analysis Arran Zeyu Wang D. Borland David Gotz CML 81 2 0 28 Aug 2024
IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark Fredrik D. Johansson CML 62 0 0 25 May 2024
A Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time Series Zhipeng Ma Marco Kemmerling Daniel Buschmann Chrismarie Enslin Daniel Lutticke Robert H. Schmitt CML 55 3 0 04 Mar 2024
Adjustment Identification Distance: A gadjid for Causal Structure Learning Leonard Henckel Theo Würtzen Sebastian Weichwald CML 95 11 0 13 Feb 2024
Proximal Causal Inference With Text Data Jacob M. Chen Rohit Bhattacharya Katherine A. Keith 70 2 0 12 Jan 2024
RCT Rejection Sampling for Causal Estimation Evaluation Katherine A. Keith Sergey Feldman David Jurgens Jonathan Bragg Rohit Bhattacharya CML 77 7 0 27 Jul 2023
$$\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery$ $\texttt{causalAssembly}$ : Generating Realistic Production Data for Benchmarking Causal Discovery Konstantin Göbler Tobias Windisch Mathias Drton T. Pychynski Steffen Sonntag Martin Roth CML 183 13 0 19 Jun 2023
Reinterpreting causal discovery as the task of predicting unobserved joint statistics Dominik Janzing P. M. Faller L. C. Vankadara CML 97 3 0 11 May 2023
NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning Muralikrishnna G. Sethuraman Romain Lopez Ramkumar Veppathur Mohan Faramarz Fekri Tommaso Biancalani Jan-Christian Hütter CML 79 12 0 04 Jan 2023
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling Romain Lopez Natavsa Tagasovska Stephen Ra K. Cho J. Pritchard Aviv Regev OOD CML DRL 100 39 0 07 Nov 2022
CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data Mathieu Chevalley Yusuf Roohani Arash Mehrjou J. Leskovec Patrick Schwab CML 98 38 0 31 Oct 2022
Large-Scale Differentiable Causal Discovery of Factor Graphs Romain Lopez Jan-Christian Hütter J. Pritchard Aviv Regev CML AI4CE 87 43 0 15 Jun 2022
Validating Causal Inference Methods Harsh Parikh Carlos Varjao Louise Xu E. T. Tchetgen CML 87 21 0 09 Feb 2022
Evaluation Methods and Measures for Causal Learning Algorithms Lu Cheng Ruocheng Guo Raha Moraffah Paras Sheth K. S. Candan Huan Liu CML ELM 100 54 0 07 Feb 2022
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
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
The Proximal ID Algorithm I. Shpitser Zach Wood-Doughty E. T. Tchetgen CML 85 17 0 15 Aug 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
Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter Pulkit Sharma Shezan Rohinton Mirzan Apurva Bhandari Anish Pimpley Abhiram Eswaran Soundar Srinivasan Liqun Shao FAtt 23 11 0 13 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
Towards a Measure of Individual Fairness for Deep Learning Krystal Maughan Joseph P. Near TDI FaML 49 5 0 28 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 104 20 0 21 Sep 2020
Causal Inference using Gaussian Processes with Structured Latent Confounders Sam Witty Kenta Takatsu David D. Jensen Vikash K. Mansinghka CML 143 19 0 14 Jul 2020
Differentiable Causal Discovery from Interventional Data P. Brouillard Sébastien Lachapelle Alexandre Lacoste Simon Lacoste-Julien Alexandre Drouin CML 87 191 0 03 Jul 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
A Ladder of Causal Distances Maxime Peyrard Robert West CML 65 6 0 05 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
Causal datasheet: An approximate guide to practically assess Bayesian networks in the real world B. Butcher V. Huang Jeremy Reffin S. Sgaier Grace Charles Novi Quadrianto CML 111 18 0 12 Mar 2020
Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition Vincent Dorie J. Hill Uri Shalit M. Scott D. Cervone CML 283 288 0 09 Jul 2017