Eviction on American Families
In this pset we’re going to continue our study of the effects of eviction on American families. This week we discussed how we can use observational data to ask causal questions. In this problem set we’re going to practice identifying and controlling for confounding variables in order to get better estimates for the average treatment effect.
Pt 1: Reading in Data (20 pts) In the code block below read in our data for this problem set: the debt dataset we’ve been working with for the past week., Also be sure to change the name of this problem set to your name both in the header above and with the filename., Remove NA observations in the debt dataset. “‘{r} # Pt 2: Identifying and Transforming Treatment and Outcome Variables (10 pts), In this problem set we are interested in estimating the average causal effect of eviction on focal child ,Pt 1: The code below creates a new variable in our debt dataset called “unsafe_perception”., This variablNote: Before you can compile this document you need to fect. Extra Credit (10 Pts) Describe below one other variable not included in the debt dataset that might confound the relationship between eviction and child perceptions of safety. We call these variables unobserved variables. Explain why this variable may confound the relationship. Remember, for a variable to confound a causal relationship it has to both have an effect on the treatment variable and have an effect on the outcome variable. Describe how this confounding variable may do this.
Pt 1: Reading in Data (20 pts) In the code block below read in our data for this problem set: the debt dataset we’ve been working with for the past week., Also be sure to change the name of this problem set to your name both in the header above and with the filename., Remove NA observations in the debt dataset. “‘{r} # Pt 2: Identifying and Transforming Treatment and Outcome Variables (10 pts), In this problem set we are interested in estimating the average causal effect of eviction on focal child ,Pt 1: The code below creates a new variable in our debt dataset called “unsafe_perception”., This variablNote: Before you can compile this document you need to fect. Extra Credit (10 Pts) Describe below one other variable not included in the debt dataset that might confound the relationship between eviction and child perceptions of safety. We call these variables unobserved variables. Explain why this variable may confound the relationship. Remember, for a variable to confound a causal relationship it has to both have an effect on the treatment variable and have an effect on the outcome variable. Describe how this confounding variable may do this.