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Knowledge Hub

Causal AI Foundational Concepts

Knowledge Hub

Confounders Fundamentally, confounders are common causes for multiple variables. These variables are hence confounded. This poses two major problems for modeling: spurious correlations and biased effect estimation. A common cause of confounded variables ofte...

Discovering Causal Relationships

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Observational Data In observational studies, researchers observe the exposure and outcome variables without any intervention or manipulation. They simply observe the data as it naturally occurs. Therefore, the distribution of potential outcomes in an observat...

Estimating Causal Effects

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Structural Causal Model Structural causal models (SCMs) are a type of model used in causal inference to represent the relationships between variables and how they cause each other. Unlike a standard ML model in which the objective is to develop predictive r...

From Models to Decisions

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Algorithmic Recourse Suppose you have constructed a causal model that is able to quantify how exercise, medication and stress management tools affect blood pressure - the natural question is: how to use this information to get the optimal intervention, for ea...

What is Causal AI?

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Causal approaches empower data scientists to answer questions that cannot be answered using standard machine learning techniques, leading to a clearer connection to ROI from their models. Examples of such questions include:-    “What is the optimal treatment t...