Causal Inference in R

In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work. This workshop is for you if you: know how to fit a linear regression model in R, have a basic understanding of data manipulation and visualization using tidyverse tools, and are interested in understanding the fundamentals behind how to move from estimating correlations to causal relationships.

Understanding Statistics in Medical Literature

In today’s fast-paced healthcare landscape, understanding data and statistics is essential for making informed decisions. Whether you’re a medical student navigating your first journal article or a healthcare professional hoping to apply the latest research to patient care, the ability to critically evaluate medical literature is a vital skill. This course is designed to introduce you to the core concepts of data and statistics, equipping you with the tools to extract meaningful insights from research without becoming bogged down in complex mathematical notation.

Causal Inference in R

In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting.

Causal Inference in R

In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting.

Causal Inference in R

This 6 week series will cover causal inference model building and evaluation techniques. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.

Causal Inference in R

In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting.

Causal Inference in R

In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.

Causal Inference in R

In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.

Causal Inference in R

In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.

Causal Inference in R

In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.