InTotalEnergies Digital FactorybyMarc HobballahDoubleML for elasticity estimationIn the rapidly evolving landscape of data science, estimating causal effects accurately remains a significant challenge, especially with…Nov 28
InTowards Data SciencebyFelix GermaineMarketing Mix Modeling (MMM): How to Avoid Biased Channel EstimatesLearn which variables you should and should not take into account in your model.Oct 164
Scott HebnerCausality in Agentic AICausality is critical to Agentic AI systems that wish to help people achieve goals, problem-solve and even act on their behalf. Explore…Nov 81Nov 81
Hoàng Thiên NữThe 5th Place Solution to the ADIA Causal Discovery Challenge 2024I recently participated in the ADIA Causal Discovery Challenge and emerged as a proud 5th place winner. Let’s dive into the exciting…Nov 27Nov 27
InTowards Data SciencebySamuele MazzantiCausality in ML Models: Introducing Monotonic ConstraintsMonotonic constraints are key to making machine learning models actionable, yet they are still quite unusedSep 612Sep 612
InTotalEnergies Digital FactorybyMarc HobballahDoubleML for elasticity estimationIn the rapidly evolving landscape of data science, estimating causal effects accurately remains a significant challenge, especially with…Nov 28
InTowards Data SciencebyFelix GermaineMarketing Mix Modeling (MMM): How to Avoid Biased Channel EstimatesLearn which variables you should and should not take into account in your model.Oct 164
Scott HebnerCausality in Agentic AICausality is critical to Agentic AI systems that wish to help people achieve goals, problem-solve and even act on their behalf. Explore…Nov 81
Hoàng Thiên NữThe 5th Place Solution to the ADIA Causal Discovery Challenge 2024I recently participated in the ADIA Causal Discovery Challenge and emerged as a proud 5th place winner. Let’s dive into the exciting…Nov 27
InTowards Data SciencebySamuele MazzantiCausality in ML Models: Introducing Monotonic ConstraintsMonotonic constraints are key to making machine learning models actionable, yet they are still quite unusedSep 612
InData Science at MicrosoftbyGanga MeghanathCausal analysis overview: Causal inference versus experimentation versus causal discoveryAn introductory overview of causal analysis describing three methodologies used to generate causal insights to power data-driven decision…Nov 52
Nadine KeilThe More People Use Sunscreen, the More People Get Skin Cancer — What We Can Learn From the…At some point companies have gathered a lot of data. They want to use that data to optimise their product or service and dive straight…Nov 11
InTowards Data SciencebyQitian WuTowards Generalization on Graphs: From Invariance to CausalityThis blog post shares recent papers on out-of-distribution generalization on graph-structured dataJul 181
