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posit::conf(2023) Workshop: Causal Inference with R
Register now: http://pos.it/conf
Instructors: Malcolm Barrett and Travis Gerke
This course 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
• are interested in understanding the fundamentals behind how to move from estimating correlations to causal relationships
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.
Видео posit::conf(2023) Workshop: Causal Inference with R канала Posit PBC
Instructors: Malcolm Barrett and Travis Gerke
This course 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
• are interested in understanding the fundamentals behind how to move from estimating correlations to causal relationships
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.
Видео posit::conf(2023) Workshop: Causal Inference with R канала Posit PBC
rstudio data science machine learning python stats tidyverse data visualization data viz ggplot technology coding connect server pro shiny rmarkdown package manager CRAN interoperability serious data science dplyr forcats ggplot2 tibble readr stringr tidyr purrr github data wrangling tidy data odbc rayshader plumber blogdown gt lazy evaluation tidymodels statistics debugging programming education rstats open source OSS reticulate
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10 мая 2023 г. 2:20:14
00:01:19
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