Double Machine Learning

Double machine learning (DML) is a causal inference technique that uses machine learning algorithms to estimate causal effects from observational data, mitigating bias from confounding variables by flexibly modeling complex relationships. Current research focuses on extending DML to handle various data structures (panel data, multimodal data), addressing challenges like unobserved heterogeneity and zero-inflated data, and improving its efficiency through coordinated learning and specialized loss functions. This methodology is proving valuable across diverse fields, enabling more robust causal analysis in areas such as economics (policy impact), marketing (customer behavior), and environmental science (impact assessments), leading to more reliable and informative insights from observational studies.

Papers

October 20, 2024

Where to Build Food Banks and Pantries: A Two-Level Machine Learning Approach
Gavin Ruan, Ziqi Guo, Guang Lin
Constructive Approach Double Machine Learning Food Security Open Checkout Free Grocery K Medoids Algorithm

September 3, 2024

Double Machine Learning at Scale to Predict Causal Impact of Customer Actions
Sushant More, Priya Kotwal, Sujith Chappidi, Dinesh Mandalapu, Chris Khawand
Visual Analogue Scale Causal Effect Causal Analysis Causal Machine Double Machine Learning

September 2, 2024

Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions
Jonathan Fuhr, Dominik Papies
Causal Effect Common Pitfall Promise Unobserved Heterogeneity Text Based Confounders Double Machine Learning Feasible Solution Panel Data Cross Fold Model Prediction Average Random Effect

July 19, 2024

GE2E-AC: Generalized End-to-End Loss Training for Accent Classification
Chihiro Watanabe, Hirokazu Kameoka
Speech Recognition Speaker Identity Double Machine Learning Accent Conversion Accent Classification

May 14, 2024

Learning Decision Policies with Instrumental Variables through Double Machine Learning
Daqian Shao, Ashkan Soleymani, Francesco Quinzan, Marta Kwiatkowska
Based Policy Instrumental Variable Double Machine Learning

March 31, 2024

Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets
Anoop Kumar, Suresh Dodda, Navin Kamuni, Rajeev Kumar Arora
Global Impact Causal Inference Financial Market Double Machine Learning Market Return Macroeconomic Policy

March 21, 2024

Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
Jonathan Fuhr, Philipp Berens, Dominik Papies
Machine Learning Estimation Task Causal Effect Causal Structure Causal Effect Estimation Evaluation Method Double Machine Learning

February 20, 2024

Causal hybrid modeling with double machine learning
Kai-Hendrik Cohrs, Gherardo Varando, Nuno Carvalhais, Markus Reichstein, Gustau Camps-Valls
Causal Inference Hybrid Model Heterogeneous Treatment Effect Hybrid Modeling Double Machine Learning Novel Causal Model

February 5, 2024

Estimation of conditional average treatment effects on distributed confidential data
Yuji Kawamata, Ryoki Motai, Yukihiko Okada, Akira Imakura, Tetsuya Sakurai
Raw Data Estimation Task Privacy Preserving State Estimation Conditional Average Treatment Effect Double Machine Learning Data Collaboration

February 1, 2024

DoubleMLDeep: Estimation of Causal Effects with Multimodal Data
Sven Klaassen, Jan Teichert-Kluge, Philipp Bach, Victor Chernozhukov, Martin Spindler, Suhas Vijaykumar
Causal Inference Estimation Task Multimodal Data Causal Effect Treatment Effect Causal Effect Estimation Causal Effect Identification Double Machine Learning

December 23, 2023

Causal Forecasting for Pricing
Douglas Schultz, Johannes Stephan, Julian Sieber, Trudie Yeh, Manuel Kunz, Patrick Doupe, Tim Januschowski
Transformer Based Causal Inference Curious Price Demand Forecasting Causal Prediction Forecasting Method Double Machine Learning

December 13, 2023

Double Machine Learning for Static Panel Models with Fixed Effects
Paul Clarke, Annalivia Polselli
Causal Inference Double Machine Learning Panel Data Fixed Effect

December 11, 2023

Extracting Self-Consistent Causal Insights from Users Feedback with LLMs and In-context Learning
Sara Abdali, Anjali Parikh, Steve Lim, Emre Kiciman
Large Language Model Context Learning Domain Knowledge Causal Graph Double Machine Learning External Feedback Causal Insight

December 7, 2023

The built environment and induced transport CO2 emissions: A double machine learning approach to account for residential self-selection
Florian Nachtigall, Felix Wagner, Peter Berrill, Felix Creutzig
Environment Feature Double Machine Learning Travel Behavior Transportation Carbon Emission

October 12, 2023

Dealing with zero-inflated data: achieving SOTA with a two-fold machine learning approach
Jože M. Rožanec, Gašper Petelin, João Costa, Blaž Bertalanič, Gregor Cerar, Marko Guček, Gregor Papa, Dunja Mladenić
Machine Learning Model State of the Art Demand Prediction Prediction Performance Double Machine Learning Zero Inflated Appliance Detection

July 14, 2023

Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learning
Byung-Kwan Lee, Junho Kim, Yong Man Ro
Adversarial Example Adversarial Robustness Adversarial Perturbation Causal Effect Estimation Adversarial Vulnerability Double Machine Learning

July 4, 2023

A Double Machine Learning Approach to Combining Experimental and Observational Data
Harsh Parikh, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
Treatment Effect Experimental Study Modeling Assumption Observational Data Double Machine Learning Observational Study

March 21, 2023

Counterfactually Fair Regression with Double Machine Learning
Patrick Rehill
AI Fairness Counterfactual Fairness Double Machine Learning Fair Regression

June 2, 2022

Coordinated Double Machine Learning
Nitai Fingerhut, Matteo Sesia, Yaniv Romano
Black Box Model Double Machine Learning Orthogonal Statistical Learning

April 30, 2022

Orthogonal Statistical Learning with Self-Concordant Loss
Lang Liu, Carlos Cinelli, Zaid Harchaoui
Non Asymptotic Double Machine Learning Orthogonal Statistical Learning