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How does decision-gravity dictate this gap? The post Causal Inference Is Different in Business appeared first on Towards Data Science.
*This is the introduction to a series on causal inference — one of the most practically important and least taught topics in data science…Continue reading on Medium »
Learn how Propensity Score Matching uncovers true causality in observational data. By finding "statistical twins," we eliminate selection bias to reveal the real impact of your int...
I want to show you a tool I just open-sourced. It's called CausalLens, and it answers one specific question that most analytics stacks get completely wrong: did this intervention a...
A practitioner's warning about generated variables in causal analysis The post LLM Themes Are Not Observations appeared first on Towards Data Science.
Imagine you’re estimating the trajectory of a ball thrown at a fixed angle and speed.Continue reading on Medium »
Turning free-to-use data into a hypothesis-ready dataset The post Using Causal Inference to Estimate the Impact of Tube Strikes on Cycling Usage in London appeared first on Towards...
Imagine this result from a marketing experiment:Continue reading on Medium »
John “Bayesian Data Analysis” Carlin writes: Recent developments in the methodology of epidemiological research have emphasized the importance of achieving clarity of purpose by cl...
Why Crypto Markets Need Causal AI — And Why Statistical Models Keep Failing Them The feedback loops
A practitioner's guide to causal attribution when two churn drivers arrive at once. The post When Customers Churn at Renewal: Was It the Price or the Project? appeared first on Tow...
Why Financial Risk Models Must Go Causal — And What the Industry Gains When They Do The financial i
A practitioner's argument that meeting summarizers fail in the same way regressions fail when you skip the part where you ask what the data can support. The post LLM Summarizers Sk...
What does correlation tells us? The post Correlation Doesn’t Mean Causation! But What Does It Mean? appeared first on Towards Data Science.
Most healthcare AI predicts who is at risk. This project explores a harder question: what sequence of actions could change a patient’s…Continue reading on Medium »
Benedikt Koch, Kosuke Imai, and Tomasz Strzalecki write: Counterfactual utilities evaluate decisions not only by the realized outcome under a given decision, but also by the counte...
Most enterprise systems are very good at answering one question: “What happened?” They are surprisingly bad at answering a more important one: “Why did it happen?”
I believe, as applied statisticians, we need to get our hands dirty and immerse ourselves in the applications we try to address. This post is mostly about medical ethics and the fa...
A data quality case study from English local elections on categorical normalisation, metric validation, and why raw labels should never define analytical groups. The post Churn Wit...
Personal Perspective: AI confidently provides answers that, often, are disconnected from reality itself, distorting how our brains interpret cause and effect.
Why the CFO's Next Hire Won't Be Human — It Will Be a Causal Graph The finance function has spent t
I am getting my master’s in data science, and during the program, I have been thinking about how often data science is used in everyday…Continue reading on Medium »
I tried to predict air pollution. The simplest model almost won. Then cross-validation showed me what was really going on.Continue reading on Medium »
by Zhaotong Lin, Wei Pan, Haoran Xue Inferring a causal network among multiple traits is essential for unraveling complex biological relationships and informing interventions. Men...
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