## Welcome to my blog

I mainly post about data analysis and applied statistics stuff, usually in R. Frequent topics include Bayesian statistics, multilevel models, and statistical power.

Written by A. Solomon Kurz

# Causal inference with beta regression

In this ninth post of the causal inference + GLM series, we explore the beta likelihood for continuous data restricted within the range of 0 to 1.

By A. Solomon Kurz

June 25, 2023

# Causal inference with change scores

In this post, we explore how we can frame our causal inferences in terms of change from baseline, and how this may or may not involve change scores.

By A. Solomon Kurz

June 19, 2023

# Causal inference with ordinal regression

In this seventh post of the causal inference series, we apply our approach to ordinal models. Ordinal models make causal inference tricky, and it’s not entirely clear what the causal estimand should even be. We explore two of the estimands that have been proposed in the literature, and I offer a third estimand of my own.

By A. Solomon Kurz

May 21, 2023

# Causal inference with gamma regression or: The problem is with the link function, not the likelihood

So far the difficulties we have seen with covaraites, causal inference, and the GLM have all been restricted to discrete models (e.g., binomial, Poisson, negative binomial). In this sixth post of the series, we’ll see this issue can extend to models for continuous data, too. As it turns out, it may have less to do with the likelihood function, and more to do with the choice of link function. To highlight the point, we’ll compare Gaussian and gamma models, with both the identity and log links.

By A. Solomon Kurz

May 14, 2023

# Causal inference with count regression

In this fifth post of the causal inference series, we practice with Poisson and negative-binomial models for unbounded count data. Since I’m a glutton for punishment, we practice both as frequentists and as Bayesians. You’ll find a little robust sandwich-based standard error talk, too.

By A. Solomon Kurz

May 7, 2023

# Causal inference with Bayesian models

In this fourth post, we refit the models from the previous posts with Bayesian software, and show how to compute our primary estimates when working with posterior draws. The content will be very light on theory, and heavy on methods. So if you don’t love that Bayes, you can feel free to skip this one.

By A. Solomon Kurz

April 30, 2023

# Causal inference with logistic regression

In this third post of the causal inference series, we switch to a binary outcome variable. As we will see, some of the nice qualities from the OLS paradigm fall apart when we want to make causal inferences with binomial models.

By A. Solomon Kurz

April 24, 2023

# Causal inference with potential outcomes bootcamp

In this second post, we learn how the potential outcomes framework can help us connect our regression models to estimands from the contemporary causal inference literature. We start with simple OLS-based models. In future posts, we’ll expand to other models from the GLM.

By A. Solomon Kurz

April 16, 2023

# Boost your power with baseline covariates

This is the first post in a series on causal inference. Our ultimate goal is to learn how to analyze data from true experiments, such as RCT’s, with various likelihoods from the generalized linear model (GLM), and with techniques from the contemporary causal inference literature. In this post, we review how baseline covariates help us compare our experimental conditions.

By A. Solomon Kurz

April 12, 2023