Causal Inference in Retail: An M&S Case Study
Explore causal inference techniques for understanding consumer behavior and improving customer lifetime value in retail, using Marks & Spencer as a case study.
Observational Causal Inference for Improving and Auditing ML in Healthcare
Explore observational causal inference techniques to enhance and audit machine learning models in healthcare, focusing on distribution shifts, safe policy learning, and robust off-policy evaluation.
Causal Discovery and Representation Learning - Day 4
Explore causal discovery and representation learning in machine learning for drug discovery with Jason Hartford's insightful presentation.
Identifying Representations for Intervention Extrapolation
Explore identifiable representations for intervention extrapolation, improving generalizability in machine learning. Learn theoretical foundations and practical applications through simulations and Q&A.
Statistical Limits of Causal Inference
Explore fundamental limits of causal effect estimation in observational data, examining personalized effects, non-smooth settings, and discrete scenarios without assuming prior causal inference knowledge.
Causal Machine Learning with CausalELM - JuliaCon 2024
Explore causal machine learning with CausalELM, covering potential outcomes, real-world examples, extreme learning machines, and estimators. Learn practical implementation for various applications without extensive statistical background.
Causal Inference
Explore causal inference concepts, methodologies, and applications in AI with expert Adèle H. Ribeiro. Gain insights into probabilistic reasoning and decision-making.
Causal Machine Learning
Explore causal machine learning concepts and techniques with expert Fredrik D. Johansson in this comprehensive ProbAI lecture.
Causal Inference - Lecture from Nordic Probabilistic AI School 2024
Explore causal inference principles, techniques, and applications in AI with expert Adele Ribeiro. Gain insights into probabilistic modeling and decision-making.
Optimizing Causal Objective Functions - Algorithms and Complexity
Explore causal objective functions, their optimization, and algorithmic complexities. Delve into structural causal models, unit selection, and variable elimination techniques for associational queries.
Causal Inference to Inform Curation Practices in Online Platforms
Explore causal inference methods for improving content curation on digital platforms. Examine case studies on Reddit, discuss implications for policy, and consider future research directions in the era of generative AI.
Causal Inference with Mendelian Randomization in Biomedical Studies
Explore Mendelian Randomization for causal inference in biomedical research. Learn innovative methods, overcome challenges, and gain insights into complex disease relationships through simulation and case studies.
Learning Causal Models via Algebraic Constraints
Explorez les modèles causaux linéaires et leurs graphes dirigés. Découvrez des méthodes algébriques pour identifier les relations causales à partir de données observationnelles, même avec des cycles ou variables cachées.
Causal Effects via Regression with Python Code - Part 5
Explore regression-based techniques for computing causal effects, including linear regression, double machine learning, and metalearners. Gain practical insights with Python code examples.
Causal Effects via DAGs: Handling Unobserved Confounders - Part 4
Explore graphical criteria for evaluating causal effects in non-Markovian models, focusing on handling unobserved confounders through back and front door criteria.
Causal Effects via the Do-operator: Overview and Example - Part 3
Explore the do-operator for estimating causal effects from observational data. Learn alternative formulations of average treatment effect and techniques for handling unmeasured confounders.
Causal Effects - Introduction to the Potential Outcomes Framework
Explore the Potential Outcomes Framework and three types of causal effects in this introductory video on causal inference, laying the groundwork for future discussions on observational data analysis.
All Causal DAGs Are Wrong but Some Are Useful
Explore the concept of causal DAGs, their usefulness, and challenges in causal discovery. Gain insights into evaluating causal models without ground truth and understanding causality's meaning across various tasks.
Omitted Variable Bias in Causal Machine Learning
Explore omitted variable bias in causal machine learning with Victor Chernozhukov's keynote address at UAI 2023, offering insights into this critical aspect of AI research.
Partial Identification of Dose Responses with Hidden Confounders - UAI 2023 Oral Session 3
Explore novel methods for bounding continuous-valued treatment effects with hidden confounders in causal inference, enhancing decision-making in observational studies.
Establishing Markov Equivalence in Cyclic Directed Graphs - UAI 2023 Oral Session 3
Explore an efficient procedure for establishing Markov equivalence in cyclic directed graphs, based on the Cyclic Equivalence Theorem and ancestral perspective.