As a data analyst, I’ve faced the challenge of estimating the impact of a marketing campaign on customer behavior. With multiple approaches at our disposal, it’s essential to understand the strengths and limitations of each method. In this post, I’ll delve into the differences between Difference-in-Differences (DiD), ANCOVA, and Propensity Score Matching (PSM) and explore which one to trust when the results don’t align.
The scenario: we have pre- and post-campaign data, along with covariates like location and gender. Our goal is to isolate the effect of the campaign on the outcome variable.
DiD seems like a natural choice, but it assumes parallel trends, which can be difficult to validate. Moreover, the assumption of parallel trends across different locations might be unrealistic due to geographical differences.
ANCOVA, on the other hand, is a more straightforward approach that involves regressing the post-campaign outcome on the pre-campaign outcome, treatment, and covariates. However, it assumes a linear relationship between the outcome and covariates, which might not always hold true.
Propensity Score Matching (PSM) takes a different route by attempting to balance the treatment and control groups based on their propensity scores. While it’s a powerful approach, it’s not immune to bias, and it can be challenging to include all relevant covariates.
In my experience, the results from these three methods can differ significantly. PSM, in particular, can overestimate the effect if it doesn’t eliminate bias during matching. So, which method should we trust?
While DiD is a popular choice, it’s essential to validate the parallel trend assumption. One way to do this is by visualizing the pre-campaign trends for both the treatment and control groups. If the trends are parallel, DiD might be a suitable approach. However, if the trends diverge, ANCOVA or PSM might be more appropriate.
Ultimately, the choice of method depends on the research question, data quality, and the ability to validate assumptions. By understanding the strengths and limitations of each approach, we can make more informed decisions about which method to use and how to interpret the results.