Maximum Lyapunov Exponent for Temporal Networks
Explore the Maximum Lyapunov Exponent concept in temporal networks, delving into complex systems analysis and network dynamics.
Learn One Size to Infer All: Exploiting Symmetries in Dynamical Systems Using Scalable Neural Networks
Explore symmetries in dynamical systems using scalable neural networks to infer patterns across various sizes and complexities.
Numerical Methods for Quasi-Stationary Distributions
Explore numerical techniques for calculating quasi-stationary distributions in complex systems, enhancing understanding of long-term behavior in stochastic processes.
Optimal Control of Lake Eutrophication
Explore strategies for managing lake ecosystems, focusing on controlling eutrophication through mathematical modeling and optimal control theory techniques.
Numbers and Art: Statistical Pitfalls and Their Consequences
Explore statistical pitfalls in data analysis, from Simpson's Paradox to hypothesis testing. Learn to critically evaluate models and metrics, understanding their real-world implications beyond simple accuracy measures.
Causal Inference in Python: From Theory to Practice
Explore causal inference in Python, moving beyond association to causation. Learn graphical causal models, do-calculus, and apply the causal data science pipeline to retail problems using DoWhy library.
Learning Gaussian DAGs from Network Data
Descubre técnicas avanzadas para aprender DAGs gaussianos a partir de datos de red con el Dr. Oscar Hernán Madrid Padilla de UCLA.
Introduction to Linear Algebra for Data Science
Explore core linear algebra concepts in data science, from vector representations to matrix factorization, with real-world applications in text similarity, image processing, and content recommendation.
Introduction to Causal Inference - Asking Your Data "Why?"
Explore causal relationships in data beyond correlation. Learn to ask "Why?" and unveil insights using simple techniques, avoiding misinterpretation pitfalls like Simpson's Paradox.
Advanced Causal Inference Made Simple
Explore automated causal inference techniques using auto-causality package. Learn to analyze A/B test results, measure impacts, and identify customer responses to different options.
Causality in a Post-Cookie World
Explore causal learning for data-driven marketing decisions in an era of privacy protection and unpredictable market conditions.
Causal Data Science in Retail - Understanding Supply Chain and Customer Behavior
Explore causal inference and Bayesian techniques in retail, focusing on supply chain, customer behavior, and promotion effects at M&S. Learn to answer "why" and "what-if" questions using machine learning.
Improving Insights by Utilizing Causal Inference Methods
Explore causal inference methods to de-bias data, approximate incremental improvements, and accelerate insights using propensity score matching and regression discontinuity.
Proving the Impact: Using Synthetic Control to Evaluate Intervention Effectiveness
Explore Synthetic Control methodology for measuring intervention impact when A/B testing isn't feasible. Learn to construct "synthetic" units, estimate causal effects, and apply this innovative approach to real-world scenarios.
Introduction to Statistics - Key Concepts and Applications
Learn fundamental statistical concepts, data types, and key terms. Explore data summarization, visualization techniques, and real-world applications in this comprehensive introduction to statistics.
Using Causal Inference to Increase a FinTech's Customer Acquisitions
Explore causal inference techniques to optimize customer acquisition strategies in FinTech, focusing on reducing disengagement during onboarding and improving contact timing.
Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations
Explore score-based generative modeling for graphs using stochastic differential equations. Learn about graph diffusion, forward and reverse processes, and applications in molecule generation.
A Causal Inference Framework for Combinatorial Interventions
Explore a novel latent factor model for estimating causal effects of combinatorial interventions, addressing challenges in factorial experiments and recommendation systems.
Causal Abstractions Using Generalized Functions
Explore generalized functions and their applications in causal models, including nondeterministic structural models and novel types of model reduction, leading to new definitions of causal abstraction.
Meaningful Causal Aggregation and Paradoxical Confounding
Explore causal aggregation challenges, macro interventions, and micro-realism in statistical analysis. Gain insights into ill-defined causality and its impact on confounding in aggregated variables.
Weakly Supervised Causal Representation Learning
Explore weakly supervised causal representation learning, its theoretical foundations, and practical applications in disentangling causal variables from unstructured data like images for improved causal reasoning.