Blog Posts - Causally Curious in the Real World

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Comparison: The Thief of Joy

Unless it’s Comparing Causal Methods!

Simulation

Causal Methods

There are a ton of causal methods that are available and it’s only increasing. To name a few: matching, inverse probability of treatment weighting, regression, machine…

Emulating a Target Trial

Lessons Learned

Target Trial

Lessons Learned

A target trial is the trial that we’d like to conduct under ideal circumstances. Emulating a target trial consists of trying to mimic this trial as much as possible given…

Missing Data

The Where’s Waldo of Causal Inference

Missing Data

MCAR

MAR

MNAR

Missing data are unavoidable when dealing with data, especially with real-world data. So it’s important to have a plan on what to do with missing data! To make valid causal…

Resampling Magic

Demystifying Bootstrapping

Resampling

Bootstrapping

Variance Estimation

A key part of any analysis is to determine the uncertainty associated with an estimate. Typically we use confidence intervals to show this. Sometimes deriving this can be…

Mastering Causal Inference Through Make-Believe

Simulating Data in R

Simulating Data

Simulating data is something that I’ve learned since graduating from my masters. It’s helped me tremendously with comparing methods and proving concepts to me. It’s also…

Hot Diggity DAG

An Introduction to DAGs

DAGs

Causal Diagrams

Yes..well no…well kinda. DAG in this case, stands for Directed Acyclic Graph. It’s a graph that is directed and acyclic…alright that’s not much of an explanation. Let’s try…

Who are we talking about?

The Role of Causal Estimands

Estimands

ATE

ATT

ATU

ATO

A key part of any research question is to figure out who our target population is. These are the people we want to study. For example, is it 85 year olds who play american…

Beyond the Odds

Unravelling the Enigma of Non-Collapsibility

OR

Collapsibility

Epidemiologists and clinical researchers use different metrics to quantify the relationship between the exposure and outcome. Commonly used measures include the risk ratio…

Standardize Your Way to Causal Inference

Standardization and the Parametric G-Formula

Standardization

Parametric G-Formula

Imagine that a teacher wants to know if giving a lesson about growing plants causes plants to grow longer. They decide to use two groups of peoples: kids (group A) and their…

Generalized Linear Models

GLM

Regression

Regression is a commonly used tool in inferential statistics to build a model to make inferences about a super-population. Before we start however, I’d like to highlight…

Inverse Probability Weighting

IPW

IPTW

Re-weighting in this context has nothing to do with weight. Instead it is a statistical method that is used to adjust for confounding to ensure exchangeability. This method…

Marginal vs Conditional Effects

Marginal

Conditional

This post is going to be about marginal compared to conditional effects. First, we need to understand what in the world these terms even mean. To do that, can you guess what…

Real-World Evidence: What’s all the hype?

RWE

Observational Data

You may have seen real-world evidence (RWE) being used increasingly in research settings. It has tremendous potential for answering important research questions, however…

P-Values and Power: Please Explain

P-Values

Hypothesis Tests

P-values are a term commonly heard in scientific literature, however often they are misconstrued, misunderstood or misinterpreted. The p in p-value stands for probability…

Intro to Causal Inference

Causal Inference

RCT

Observational Data

I was first introduced to causal inference by Hernan and Robins (2021), in the summer of 2021 prior to starting my PhD. This book was tremendous and changed my thinking…

OR/RR/HR: What’s the Difference?

OR

RR

HR

Let’s start with probability because that’s probably a good place to start. Probability is basically how likely something is to occur. For example, what are the chances that…

Welcome To My Blog

This is my first post in this blog! The aim of this blog is to describe methods related to real-world evidence, causal inference and statistical fun. My goal for this is to…