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{{short description|Statistical technique to use observational data for causal analysis}}
'''Difference in differences''' ('''DID'''<ref>{{cite journal |last=Abadie |first=A. |year=2005 |title=Semiparametric difference-in-differences estimators |journal=[[Review of Economic Studies]] |volume=72 |issue=1 |pages=1–19 |doi=10.1111/0034-6527.00321 |citeseerx=10.1.1.470.1475 |s2cid=8801460 }}</ref> or '''DD'''<ref name=Bertrand>{{cite journal |last1=Bertrand |first1=M. |last2=Duflo |first2=E. |author-link2=Esther Duflo |last3=Mullainathan |first3=S. |year=2004 |title=How Much Should We Trust Differences-in-Differences Estimates? |journal=[[Quarterly Journal of Economics]] |volume=119 |issue=1 |pages=249–275 |doi=10.1162/003355304772839588 |s2cid=470667 |url=http://www.nber.org/papers/w8841.pdf }}</ref>) is a [[statistics|statistical technique]] used in [[econometrics]] and [[quantitative research]] in the social sciences that attempts to mimic an [[experiment|experimental research design]] using [[observational study|observational study data]], by studying the differential effect of a treatment on a 'treatment group' versus a '[[control group]]' in a [[natural experiment]].<ref>{{cite book |last1=Angrist |first1=J. D. |last2=Pischke |first2=J. S. |year=2008 |title=Mostly Harmless Econometrics: An Empiricist's Companion |publisher=Princeton University Press |isbn=978-0-691-12034-8 |pages=227–243 |url=https://books.google.com/books?id=ztXL21Xd8v8C&pg=PA227 }}</ref> It calculates the effect of a treatment (i.e., an explanatory variable or an [[independent variable]]) on an outcome (i.e., a response variable or [[dependent variable]]) by comparing the average change over time in the outcome variable for the treatment group to the average change over time for the control group. Although it is intended to mitigate the effects of extraneous factors and [[selection bias]], depending on how the treatment group is chosen, this method may still be subject to certain biases (e.g., [[regression to the mean|mean regression]], [[Reverse causality bias|reverse causality]] and [[omitted variable bias]]).
In contrast to a [[time series|time-series estimate]] of the treatment effect on subjects (which analyzes differences over time) or a cross-section estimate of the treatment effect (which measures the difference between treatment and control groups), difference in differences uses [[panel data]] to measure the differences, between the treatment and control group, of the changes in the outcome variable that occur over time.
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