New blog post explaining the most common quantities we're interested in with Causal Inference in practice! Check it out:

medium.com/@causai_busi...

Cross validation doesn't work like usual for causal inference, because we don’t have ground truths for causal effects to compare our predictions against. 

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A great paper on the intersection and/or tension between traditional econometrics and modern causality.

economics.mit.edu/sites/defaul...

Even infinite, perfect data wouldn’t make it easy to uncover causal effects.
Why? Because observational data alone never reveals causality. Read more in our latest blog post.

medium.com/@causai_busi...

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"You are smarter than your data. Data do not understand causes and effects; humans do."

One of the best quotes ever by Judea Pearl

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One of the most underrated concepts in observational causal inference is sensitivity analysis. Sensitivity analysis helps you shift the conversation from “no confounder exists” to “it's unrealistic a confounder of strength X exists”. That’s a much more defensible claim.

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If you think you perform causal analysis without using a Causal Graph, you're still using a Causal Graph, you just didn't draw it.

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With Causal Inference, we often draw Causal DAG's. If this DAG is incorrect in relevant parts, our analysis will be off. Unfortunately, we can never fully validate our Causal Graph. How do we deal with this uncertainty? Read our latest blogpost:

medium.com/@causai_busi...

Amazing free Causal Inference book available online

chapters.causalml-book.org/CausalML_boo...

Machine Learning on its own can’t reveal causal effects, but it’s very good at modelling associations in data. Causal Inference is the field that gives us tools to move from association to causation, but still needs models to estimate the effects. Best of both worlds? Causal ML.

Learning Causal Inference should be a Data Scientist’s priority because Causality is at the heart of decision-making, and better decision-making is at the heart of Data Science.

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