<![CDATA[Data For Science: Causal Inference]]>https://data4sci.substack.com/s/causal-inferencehttps://substackcdn.com/image/fetch/$s_!4Dtu!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fc84f944b-a7ca-472c-bccb-7f9f18ad0bfa_540x540.pngData For Science: Causal Inferencehttps://data4sci.substack.com/s/causal-inferenceSubstackSun, 16 Aug 2026 04:54:26 GMT<![CDATA[Mediation]]>https://data4sci.substack.com/p/mediationhttps://data4sci.substack.com/p/mediationSat, 13 Feb 2021 14:07:00 GMTCausal Inference

This is the fifteenth post on the series we work our way through “Causal Inference In Statistics” a nice Primer co-authored by Judea Pearl himself.

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You can find the previous post here and all the we relevant Python code in the companion GitHub Repository:

Causality GitHub

While I will do my best to introduce the content in a clear and accessible way, I highly recommend that you get the book yourself and follow along. So, without further ado, let’s get started!

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]]><![CDATA[Inverse Probability Weighing]]>https://data4sci.substack.com/p/inverse-probability-weighinghttps://data4sci.substack.com/p/inverse-probability-weighingSun, 20 Dec 2020 14:05:00 GMTThis is the fourteenth post on the series we work our way through “Causal Inference In Statistics” a nice Primer co-authored by Judea Pearl himself.

Amazon Affiliate Link

You can find the previous post here and all the we relevant Python code in the companion GitHub Repository:

Causality GitHub

While I will do my best to introduce the content in a clear and accessible way, I highly recommend that you get the book yourself and follow along. So, without further ado, let’s get started!

Read more

]]><![CDATA[Conditional Interventions and Covariate-Specific Effects]]>https://data4sci.substack.com/p/conditional-interventions-and-covariatehttps://data4sci.substack.com/p/conditional-interventions-and-covariateSun, 06 Dec 2020 14:02:00 GMTThis is the thirteenth post in the series, in which we work our way through “Causal Inference In Statistics,” a nice Primer co-authored by Judea Pearl himself.

Amazon Affiliate Link

You can find the previous post here and all the we relevant Python code in the companion GitHub Repository:

Causality GitHub

While I will do my best to introduce the content in a clear and accessible way, I highly recommend that you get the book yourself and follow along. So, without further ado, let’s get started!

Read more

]]><![CDATA[Front-door Criterion]]>https://data4sci.substack.com/p/front-door-criterionhttps://data4sci.substack.com/p/front-door-criterionSun, 15 Nov 2020 13:59:00 GMT
Photo by This Guy on Unsplash

This is the twelfth post on the series we work our way through “Causal Inference In Statistics” a nice Primer co-authored by Judea Pearl himself.

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]]><![CDATA[Backdoor Criterion]]>https://data4sci.substack.com/p/backdoor-criterionhttps://data4sci.substack.com/p/backdoor-criterionSun, 08 Nov 2020 13:55:00 GMTThis is the eleventh post on the series we work our way through “Causal Inference In Statistics” a nice Primer co-authored by Judea Pearl himself.

Amazon Affiliate Link

You can find the previous post here and all the we relevant Python code in the companion GitHub Repository:

Causality GitHub

While I will do my best to introduce the content in a clear and accessible way, I highly recommend that you get the book yourself and follow along. So, without further ado, let’s get started!

Read more

]]><![CDATA[The Adjustment Formula]]>https://data4sci.substack.com/p/the-adjustment-formulahttps://data4sci.substack.com/p/the-adjustment-formulaSun, 01 Nov 2020 13:51:00 GMTThis is the tenth post on the series we work our way through “Causal Inference In Statistics” a nice Primer co-authored by Judea Pearl himself.

Amazon Affiliate Link

You can find the previous post here and all the we relevant Python code in the companion GitHub Repository:

Causality GitHub

While I will do my best to introduce the content in a clear and accessible way, I highly recommend that you get the book yourself and follow along. So, without further ado, let’s get started!

Read more

]]><![CDATA[Interventions]]>https://data4sci.substack.com/p/interventionshttps://data4sci.substack.com/p/interventionsSun, 18 Oct 2020 12:48:00 GMT Read more

]]><![CDATA[Model Testing and Causal Search]]>https://data4sci.substack.com/p/model-testing-and-causal-searchhttps://data4sci.substack.com/p/model-testing-and-causal-searchSun, 11 Oct 2020 12:43:00 GMT Read more

]]><![CDATA[d-separation]]>https://data4sci.substack.com/p/d-separationhttps://data4sci.substack.com/p/d-separationSun, 13 Sep 2020 19:17:00 GMT Read more

]]><![CDATA[Colliders]]>https://data4sci.substack.com/p/collidershttps://data4sci.substack.com/p/collidersSun, 06 Sep 2020 19:09:00 GMT
Photo by Abraham Barrera on Unsplash

This is the sixth post on the series we work our way through “Causal Inference In Statistics” a nice Primer co-authored by Judea Pearl himself.

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]]><![CDATA[Chains, and Forks]]>https://data4sci.substack.com/p/chains-and-forkshttps://data4sci.substack.com/p/chains-and-forksSun, 16 Aug 2020 19:05:00 GMT Read more

]]><![CDATA[Structural Causal Models]]>https://data4sci.substack.com/p/structural-causal-models-e4dhttps://data4sci.substack.com/p/structural-causal-models-e4dSat, 25 Jul 2020 19:01:00 GMT Read more

]]><![CDATA[Graphs]]>https://data4sci.substack.com/p/graphshttps://data4sci.substack.com/p/graphsSat, 18 Jul 2020 18:56:00 GMT Read more

]]><![CDATA[Probability Theory]]>https://data4sci.substack.com/p/probability-theoryhttps://data4sci.substack.com/p/probability-theorySun, 23 Feb 2020 19:49:00 GMT
Photo by Erica Li on Unsplash

This is the second post on the series we work our way through “Causal Inference In Statistics” a nice Primer co-authored by Judea Pearl himself.

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]]><![CDATA[Simpsons Paradox]]>https://data4sci.substack.com/p/simpsons-paradoxhttps://data4sci.substack.com/p/simpsons-paradoxSun, 02 Feb 2020 19:45:00 GMT
Photo by Ashley Jurius on Unsplash

How do causes lead to effects? Can you associate the cause leading to the observed effect? Big Data opens the doors for us to be able to answer questions such as this, but before we are able to do so, we must dive into the field of Causal Inference, a field championed by Judea Pearl.

In this series of blog posts we will learn about the main ideas of Causality by working our way through “Causal Inference In Statistics” a nice Primer co-authored by Pearl himself.

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