Difference in Difference estimation is a linear regression methodology used to analyse the effect of some event in time (the treatment) by comparing results over time. The idea is to compare data before and after the treatment. If the treatment was effective then you will find material differences in the outcomes for the target variable, or Y.
Before and After. This is a basic comparison of means for the time periods before and after treatment. For this specification we assume the target (Y) in the post-treatment period is equal to the target (Y) in the pre-treatment period in the absence of treatment. Thus, any change is attributable to the treatment.
Difference in Difference estimation is the natural extension of the before and after analysis is to include a control group for comparison. The difference in difference specification allows us to do this. We can compare Y as we did for the before and after analysis. We can also compare Ys between treated and untreated groups. To complete a difference in difference specification we use two dummy variables that partition the sample into four groups. The first dummy variable, treatment, partitions the sample in two halves based on their treatment status. The second dummy variable, post, partitions the halves in quarters based on the time period. We then interact treatment and post (Post*Treatment); the coefficient on Post*Treatment estimates the statistical difference in Y.
Y = B0 + B1*Post + B2*Treatment + B3*Post*Treatment.
McKinsey says there will be a shortage of data skills in 2018. Mckinsey predicts a shortfall in meeting the demand for 1.5 Million Data Savvy Managers. Savvy managers can make use of data on the execution side, putting insight into context and making things happen.
A major hurdle to iterating and improving strategic data driven decision making is people. Data analytics is pretty straight forward; i.e. math is just that, math. It's people (humans) that is the problem. Which means people (could that be you?) are the solution. Data science relies heavily on statistical computing. Scripts and math. Algorithms. If (1) you start with good data and (2) you have a competent data scientist conduct and interpret the analysis, you still need (3) to put those results into context; make something happen. Someone has to do! Teams (doers) need to execute on insights.
Listen. Understand the problems your team, senior, & mid-level managers are facing.
Ask great questions. Frame the problem into a set of questions that, if answered, direct action. Understand (& communicate) that decisions must be made once these questions are answered.
Understand data science. Take a survey level course on data science. LinkedIn Learning offers a course that you can get through in an afternoon. When you understand the process you can ask actionable questions that lend themselves to be answered with a data model.
Evaluate alternatives. Data often suggests multiple approaches; assemble the right team that can prioritize them.
Acknowledge and mitigate bias. Team members have (and use) inherent bias. Teams that manage GroupThink will naturally make better evaluations.
Catalyze change. Communicate and empower decisions throughout the organization. Building the architecture need for changes to take place.
These six skills are crucial to developing processes that:
(1) generate meaningful questions
(2) pose those questions effectively
(3) build understanding around data driven decisions
(4) create a culture that can implement those decisions.
Data Science requires rare (specialist) qualities:
(1) an ability to take unstructured data and find order, meaning, and value.
(2) Deep analytical talent.
Data Savvy doesn't.
To be a generalist, a data savvy manager, doesn’t. Data savvy doesn't require you to be a math expert,
learn more @ www.assume-wisely.com/data-savvy-manager