Prefer Hicks – he establishes causality by isolating patent approval as the only independent variable

//DH

The fourth and final case in the eligibility quartet—Alice Corp. v. CLS Bank International—has been the most influential. The controversy that accompanied the decision was matched by the immediacy of its impact. Following the case, a wave of patentable-subject-matter litigation swept federal courts. In the succeeding five years, hundreds of patent invalidations were handed down, spawning complaints from litigants of inconsistency and unpredictability in patentable-subject-matter jurisprudence.92 The response from the PTO—the agency responsible for examining patent applications— was even swifter. On June 25, 2014, six days after Alice was handed down, the PTO issued its initial informal guidance to examiners.93 Following the guidance, there was a precipitous increase in application rejections in the affected PTO examination units. As I show in Part IV, the average allowance rate in the three business-methods technology centers fell from around 45% to less than 10% in the months following the decision.94

This legal setting provides a unique opportunity to observe the effects of a stark and unexpected change in the PTO’s examination practices. Applicants who received a final decision before June 25, 2014 were subject to a far more lenient patentability standard than those who received a decision afterwards. But patent applications typically take several years to be resolved, and applicants cannot control their examination dates with great precision.95 In other words, in a small window of time around the PTO’s change in standards, the side of the line that an applicant fell on was effectively random. By tracking patenting and funding outcomes for these two groups of applicants—who differ, on average, only in the likelihood that they were granted a patent—we can isolate the role that the patent itself plays in securing subsequent funding.

To put this quasi-experiment into practice, I use a statistical tool known as regression discontinuity (RD).96 The idea behind an RD is simple and intuitive. It is often difficult to estimate the effect of a specific law or policy simply by comparing those who were subject to it and those who were not, because the two groups likely differ in many other respects. Some of these other factors may be related to the likelihood of being targeted by the law in the first place, while others may not be easy to observe. However, in a situation where the policy of interest is implemented at some well-defined threshold that the affected groups can’t easily control—for example, the value of a credit score or income, or a particular date—the people or companies close to the threshold on either side are likely to be similar on average in all ways except for the fact of treatment. By taking the difference in outcomes at the threshold for the groups on the left and right of that cutoff, we can obtain a credible estimate of the effect of the intervention alone. In this way, discontinuity designs can provide compelling quasi-experimental evidence of the effect of a law or policy change.97