List: Causality | Curated by Aleksander Molak

Sep 30, 2024

131 stories

18 saves

CausalityArticles on causal discovery and inference
In
TDS Archive
by
Ryan O'Sullivan
Mastering Marketing Mix Modelling In PythonPart 1 of a hands-on guide to help you master MMM in pymc
Sep 26, 2024
7
Sep 26, 2024
7
Robson Tigre
When and how to apply causal inference in time seriesIntuition, step-by-step script, and limitations of the CausalImpact and CausalArima methods
Sep 17, 2024
2
Sep 17, 2024
2
 This story is no longer available
In
tech-at-instacart
by
Levi Boxell
Using Surrogate Indices to Estimate Long-Run Heterogeneous Treatment Effects of Membership…Authors: Levi Boxell, Changyao Chen, Rustin Partow, Mack Sweeney
Aug 26, 2024
3
Aug 26, 2024
3
In
TDS Archive
by
Zachary Raicik
Sequential Testing: The Secret Sauce for Low-Volume A/B TestsHow to Accelerate Decision-Making with Low Volume Data
Aug 29, 2024
3
Aug 29, 2024
3
In
UX Collective
by
Pavel Samsonov
Nike’s $25B blunder shows us the limits of “data-driven”The illusion of data as “objective” conceals that it rarely shows you the whole picture. Making decisions based on the easiest data to…
Jan 29
118
Jan 29
118
In
Booking.com Data Science
by
Nils Skotara
Sequential Testing at Booking.comReliable and fast product decisions
Jun 12, 2023
4
Jun 12, 2023
4
In
Netflix TechBlog
by
Netflix Technology Blog
Round 2: A Survey of Causal Inference Applications at NetflixAt Netflix, we want to ensure that every current and future member finds content that thrills them today and excites them to come back for…
Jun 6, 2024
5
Jun 6, 2024
5
In
TDS Archive
by
Tim Sumner
A New Coefficient of CorrelationWhat if you were told there exists a new way to measure the relationship between two variables just like correlation except possibly…
Mar 31, 2024
60
Mar 31, 2024
60
In
Causality in Data Science
by
Jakob Runge
Hands-on Causal Discovery with PythonA Gentle Guide to Causal Inference with Machine Learning Pt. 9
Mar 4, 2024
4
Mar 4, 2024
4
In
Netflix TechBlog
by
Netflix Technology Blog
Sequential A/B Testing Keeps the World Streaming Netflix
Part 1: Continuous DataMichael Lindon, Chris Sanden, Vache Shirikian, Yanjun Liu, Minal Mishra, Martin Tingley
Feb 12, 2024
7
Feb 12, 2024
7
 This story is no longer available
Elliot Stein
Dynamic Causal ModellingUncertainty aware, explainable machine learning.
Jan 2, 2023
1
Jan 2, 2023
1
In
Causality in Data Science
by
Kenneth Styppa
Causal Discovery with Multivariate Time Series DataA Gentle Guide to Causal Inference with Machine Learning Pt. 8
Jan 8, 2024
1
Jan 8, 2024
1
Tobias
Personalized Medicine — What can it do for you and why is it not here yet?Over the last decades, health spending has increased over 31-fold in the last four decades, from $353 per person in 1970 to $11,582 in…
Jan 14, 2022
Jan 14, 2022
Shivangi Choudhary
Causal Inference techniques for Panel Data in Retail — Part IIHello readers! In my previous article on Comprehensive Guide on Causal Inference in Retail — Part I , I discussed about the assumptions …
Dec 26, 2023
Dec 26, 2023
In
TDS Archive
by
Matteo Courthoud
Understanding Group Sequential TestingHow to run valid experiments, with peeking and early stopping.
Dec 26, 2023
6
Dec 26, 2023
6
In
Walmart Global Tech Blog
by
Moitreyee Dasgupta
How Walmart Leverages CUPED and Reduces Experimentation LifecycleWritten by Moitreyee Dasgupta, Leihua Ye
Dec 7, 2023
1
Dec 7, 2023
1
In
TDS Archive
by
Matteo Courthoud
Understanding Instrumental VariablesHow to estimate causal effects when you cannot randomize treatment
Nov 13, 2023
4
Nov 13, 2023
4
Lucile Ter-Minassian
BICauseTree: Bias-balancing Interpretable Causal TreeA model for interpretable estimation of population-level causal effect that can handle high-dimensional datasets.
Jun 17, 2023
2
Jun 17, 2023
2

Causality

Articles on causal discovery and inference

Mastering Marketing Mix Modelling In Python

Part 1 of a hands-on guide to help you master MMM in pymc

When and how to apply causal inference in time series

Intuition, step-by-step script, and limitations of the CausalImpact and CausalArima methods

Using Surrogate Indices to Estimate Long-Run Heterogeneous Treatment Effects of Membership…

Authors: Levi Boxell, Changyao Chen, Rustin Partow, Mack Sweeney

Sequential Testing: The Secret Sauce for Low-Volume A/B Tests

How to Accelerate Decision-Making with Low Volume Data

Nike’s $25B blunder shows us the limits of “data-driven”

The illusion of data as “objective” conceals that it rarely shows you the whole picture. Making decisions based on the easiest data to…

Sequential Testing at Booking.com

Reliable and fast product decisions

Round 2: A Survey of Causal Inference Applications at Netflix

At Netflix, we want to ensure that every current and future member finds content that thrills them today and excites them to come back for…

A New Coefficient of Correlation

What if you were told there exists a new way to measure the relationship between two variables just like correlation except possibly…

Hands-on Causal Discovery with Python

A Gentle Guide to Causal Inference with Machine Learning Pt. 9

Sequential A/B Testing Keeps the World Streaming Netflix Part 1: Continuous Data

Michael Lindon, Chris Sanden, Vache Shirikian, Yanjun Liu, Minal Mishra, Martin Tingley

Dynamic Causal Modelling

Uncertainty aware, explainable machine learning.

Causal Discovery with Multivariate Time Series Data

A Gentle Guide to Causal Inference with Machine Learning Pt. 8

Personalized Medicine — What can it do for you and why is it not here yet?

Over the last decades, health spending has increased over 31-fold in the last four decades, from $353 per person in 1970 to $11,582 in…

Causal Inference techniques for Panel Data in Retail — Part II

Hello readers! In my previous article on Comprehensive Guide on Causal Inference in Retail — Part I , I discussed about the assumptions …

Understanding Group Sequential Testing

How to run valid experiments, with peeking and early stopping.

How Walmart Leverages CUPED and Reduces Experimentation Lifecycle

Written by Moitreyee Dasgupta, Leihua Ye

Understanding Instrumental Variables

How to estimate causal effects when you cannot randomize treatment

BICauseTree: Bias-balancing Interpretable Causal Tree

A model for interpretable estimation of population-level causal effect that can handle high-dimensional datasets.

Aleksander Molak