On the parallel trends assumption in difference-in-differences (diff-in-diffs)

“The man who has fed the chicken every day throughout its life at last wrings its neck instead, showing that more refined views as to the uniformity of nature would have been useful to the chicken.”
        – Bertrand Russell (1912/2001, p. 35)

The parallel trends assumption of difference-in-differences (diff-in-diffs) is that the average outcomes for intervention and comparison groups would have continued in parallel from pre- to post-intervention if the intervention had not been introduced. This assumption cannot be directly tested, since when diff-in-diffs is used, the intervention is introduced. However, a case is often made that parallel trends probably holds (or doesn’t not hold) by analysing pre-intervention trends.

The mystery graph below shows an example from Kahn-Lang and Lang (2020, p. 618), redrawn to add some suspense:

The averages for the two groups (A and B) are practically identical and remain parallel. I can also reveal that there is a large number of observations – enough to be really confident that the lines are parallel. Given data like this, many of us would be confident that we had found no evidence against parallel trends.

Alas, once the time series is extended, we see that the averages significantly diverge. Adding titles to the graph reveals why – it shows median height by gender from the ages 5 to 19:

Growth reference data from WHO; see percentiles for girls and boys xlsx files over here

Around age 9, the median girls’ height begins to exceed boys’, the difference peaking at about 12 years old. Then the difference in medians decreases until around 13 when boys’ median height begins to exceeds girls’.

Clearly, if we wanted to evaluate, e.g., an intervention to boost children’s height, we wouldn’t compare the mean height of one gender with another as control. The biological processes underpinning gender differences in pubertal growth spurt are well-known. However, diff-in-diffs is often applied in situations where much less is known about the dynamics of change over time.

As this example illustrates, the more we know about the topic under investigation and the more covariates we have at our disposal for choosing comparison units, the better our causal estimates using diff-in-diffs are likely to be. Diff-in-diffs can also be combined with matching or weighting on covariates to help construct a comparison group such that parallel trends is more likely to hold; see, e.g., Huntington-Klein (2022, Section 18.3.2).


Huntington-Klein, N. (2022). The effect: An introduction to research design and causality. CRC Press.

Kahn-Lang, A., & Lang, K. (2020). The Promise and Pitfalls of Differences-in-Differences: Reflections on 16 and Pregnant and Other Applications. Journal of Business & Economic Statistics, 38(3), 613–620.

Russell, B. (1912/2001). The problems of philosophy. Oxford University Press.