Use matching, weighting, propensity scores, and stratification to prepare data for causal analysis.
One of the biggest challenges data scientists face is having sparse data to answer your question. This course will introduce you to some of the most common techniques for balancing data into treatment and control groups, estimating treatment effects, and making the most of the data that you do have.
### Take-Away Skills
In this course, you will learn how to use matching, weighting, and stratification techniques to prepare the data for causal analysis. You will be able to calculate propensity scores for unobserved data with observed data, and you will learn how to balance treatment and control groups. You will learn how to use the data that we have to estimate what we don't.
One of the biggest challenges data scientists face is having sparse data to answer your question. This course will introduce you to some of the most common techniques for balancing data into treatment and control groups, estimating treatment effects, and making the most of the data that you do have.
### Take-Away Skills
In this course, you will learn how to use matching, weighting, and stratification techniques to prepare the data for causal analysis. You will be able to calculate propensity scores for unobserved data with observed data, and you will learn how to balance treatment and control groups. You will learn how to use the data that we have to estimate what we don't.
