You can use Actable AI's no-code Data Science platform to estimate if an individual goes to certain training program how that training would impact that individuals future earnings. This kind of insights could help you better understand the impact of any training program and if its effectiveness justifies the effort and investment.
In this specific example, we use our causal inference to estimate the causal relationship between a job training program and the program attendees real life earning. As an example dataset, we are using a classical dataset from Lalonde 1986 widely used in academia. The video below shows the results of our casual inference analytics estimating whether attending a job training program (treat) has any impact on your real earnings (re78).
The dataset consist of 445 observations on the 12 variables. More info about the dataset can be found here: Lalonde 1986 and Dehejia & Wahba 1999 scientific studies. Below are the 12 variables and their descriptions.
age: age in years
educ: years of schooling
black: indicator variable for blacks
hisp: indicator variable for hispanics
married: indicator variable for martial status
nodegr: indicator variable for high school diploma
re74: real earnings in 1974
re75: real earnings in 1975
re78: real earnings in 1978
u74: indicator variable for earnings in 1974 being zero
u75: indicator variable for earnings in 1975 being zero
treat: an indicator variable for treatment status
