Carryover and Shape Effects in Media Mix Modeling: Paper Review

In the following post, I’ll review and implement the main portions of “Bayesian Methods for Media Mix Modeling with Carryover and Shape Effects”.

Bias Correction For Paid Search In Media Mix Modeling: Paper Review

Bias Correction For Paid Search In Media Mix Modeling: Paper Review. This post provides a high-level overview of “Bias Correction For Paid Search In Media Mix Modeling”, providing code and implementation of key concepts.

Synthetic Instrumental Variables

This article assumes basic familiarity with causal DAGs and causal inference.Additionally, while there are many ways of creating synthetic instruments, this article uses probabilistic PCA as it’s one of the most generic methods.

Causal Impact R Package in Python

How to run Google’s CausalImpact R package from your Python environment. CausalImpact is an R package developed by Google for causal inference using Bayesian Structural time-series models. You can find the R version here.

Microsoft’s DoWhy is a Cool Framework for Causal Inference

Inspired by Judea Pearl’s do-calculus for causal inference, the open source framework provides a programmatic interface for popular causal inference methods.

Confounding Variable and Spurious Correlation: Key Challenge

Desire to solve problems is perhaps natural to all humans. The inability to identify the causes of a problem, particularly in case of the issues relevant to our personal and social lives, creates some kind of discomfort within our minds.

Using Granger Causality Test to Know If One Time Series Is Impacting?

Granger causality test is used to determine if one time series will be useful to forecast another variable by investigating causality between two variables in a time series. The method is a probabilistic account of causality; it uses observed data sets to find patterns of correlation.

Causal Inference — A Brief Introduction

Suppose we are given data from a local research laboratory about the success rates of two treatments on patients who exercise and don’t exercise.

An Application of Causal Inference

In this article, we will apply causal inference techniques to a dataset collected for the Infant Health and Development Program (IHDP). Researchers instructed trained personnel.

What do the Coefficients of a Regression Model Indicate?

“Correlation does not (necessarily) imply causation,” you must have heard this famous sentence in case you took an introductory inferential statistics/data science class.

Causal inference for data scientists: a skeptical view

How and why causal inference fails us. The purpose of this post is to show why causal inference is hard, how it fails us, and why DAGs don’t help.