Causal

Improve your causal IQ and build trust in your causal inference by mastering identificationPhoto by Paul Skorupskas on UnsplashCausal inference is the process by which we use data to make claims about causal relationships, thus it is among the core tasks of data scientists. Behind this process are two distinct concepts: identification and estimation, and only by mastering both can we get better at establishing causality from associations observed in the data.However, as new estimation methods continue to emerge data…

How to use random forests to do policy targetingCover, image by Author.In my previous blog post, we have seen how to use causal trees to estimate the heterogeneous treatment effects of a policy. If you haven’t read it, I recommend reading that first since we will take that article's content for granted and start from there.Why heterogenous treatment effects (HTE)? First of all, the estimation of heterogeneous treatment effects allows us to select which users (patients, users, customers, … ) to offer treatment (a drug, ad,…

A full explanation of causal inference front-door adjustment with examples including all the Python source codePhoto by Evelyn Paris on UnsplashBy the end of this article you will understand the magic of causal inference front-door adjustment that can calculate the effect of an event on an outcome even where there are other factors affecting both that are unmeasured or even unknown and you will have full access to all the Python code.I have scoured the Internet and many books trying to find a fully working example of the…

How to use regression trees to estimate heterogeneous treatment effects.Cover image, generated by Author using NightCaféIn causal inference, we are usually interested in estimating the causal effect of a treatment (a drug, ad, product, …) on an outcome of interest (a disease, firm revenue, customer satisfaction, …). However, knowing that a treatment works on average is often not sufficient and we would like to know for which subjects (patients, users, customers, …) it works better or worse, i.e. we would like to estimate…

IntroductionWhat makes a song tick? It’s easy to justify your love for a song when the artist hits a high note or recites a thought-provoking verse. It’s also easy to like a song solely because it was performed by one of your favorite artists. However, that alone does not account for the current music landscape. In this saturated market, where countless tracks have similar voices, genres, and styles, some tracks just happen to outperform others.This begs the question: are there more hidden/latent audio factors that…

A fully working example of the backdoor adjustment formula using Python and the pgmpy libraryPhoto by Roberto Huczek on UnsplashIn probability theory it is very straightforward to look at a dataset and calculate the probability of an event based on knowing something about other variables.For example:i.e. the probability of a sale is equal to the probability of a click on the link given that the product has been searched.However, this approach breaks down when causal effects exist in the data and this is where causal…

Understanding the roots of inverse propensity weighting through a simple example.Photo by Diego PH on UnsplashOne of the well-established methods for causal inference is based on the Inverse Propensity Weighting (IPW). In this post we will use a simple example to build an intuition for IPW. Specifically, we will see how IPW is derived from a simple weighted average in order to account for varying treatment assignment rates in causal evaluation.Let’s consider the simple example where we want to estimate the average effect…

A guide to avoiding the common pitfalls of event studiesPhoto by Ricardo Gomez Angel on UnsplashEvent studies are useful tools in the context of causal inference. They are used in quasi-experimental situations. In these situations, the treatment is not randomly assigned. Thus, in contrast to randomized experiments (i.e., A/B tests), one cannot rely on a simple comparison of the means between groups to make causal inferences. In these types of situations, event studies are very useful.Event studies are also frequently used…

An Intuitive Explanation of IPTW and a Comparison to Multivariate RegressionPhoto by Nadir sYzYgY on UnsplashIn this post I will provide an intuitive and illustrated explanation of inverse probability of treatment weighting (IPTW), which is one of various propensity score (PS) methods. IPTW is an alternative to multivariate linear regression in the context of causal inference, since both attempt to ascertain the effect of a treatment on an outcome in the presence of confounds. It is important to note the current evidence…

Applying synthetic control with a Bayesian twist to quantify the impact of a tweet (using CausalPy)Image by Tolga Aslantürk at PexelsOctober 2022 brought a lot of novelty to Twitter’s Headquarters in San Francisco (and a sink). Elon Musk, the CEO of Tesla and SpaceX became the new owner and CEO of the company on October 27.Some audiences welcomed the change warmly while others remained skeptical.A day later, on October 28, Musk tweeted “the bird is freed”.How powerful a tweet can be?Let’s see!Image by Laura Tancredi at…