On December 24, 2014, a federal district judge in Alexandria, Virginia set aside a $101 million False Claims Act (FCA) jury verdict against Gosselin World Wide Moving.

The decision held that FCA liability cannot be imposed based on anticompetitive conduct alone because the FCA is not an all-purpose anti-fraud statute, and liability requires deception or deceit in connection with a specific, identifiable claim for government payment.

In a client advisory, Arnold & Porter partner Drew Harker, along with the firm’s David Fauvre and Sean Hennessy,  say that “the court’s decision serves as support for the proposition that liability under the FCA must be tethered to the presence of identifiable claims for payment submitted to the government.”

“As the Court pointed out, the FCA is neither an antitrust statute nor an all-purpose anti-fraud statute,” they said. “Instead,  it is ‘a fraud-based statute that requires deception or deceit in connection with’ a false claim for government payment. “This decision may support arguments to defeat broad interpretations of FCA liability advanced by the government or private relators.”

The Arnold & Porter team said that the decision is also notable for its treatment of statistical modeling used to extrapolate damages under the FCA.

“In recent decisions, federal courts have been receptive to the use of statistical techniques to not only calculate damages, but also to establish FCA liability,” they said. “As private relators and the government continue to pursue broader theories of FCA liability, these types of statistical techniques will become increasingly prevalent.  It is helpful, therefore, that the Court’s analysis provides guidance for challenging the reliability of statistical techniques.  For example, the case may support arguments  that statistical models are unsuitable for extrapolating damages in cases involving markets that are not driven by traditional economic measures such as supply and demand.  The case may also provide grounds for attacking the reliability of statistical models under Daubert where modeling lacks key explanatory variables or is incapable of accurately forecasting real-world outcomes.”