Science and Law
27th December 2012

Establishing Causation in the Courtroom – A Basic Introduction

As every lawyer working on personal injury cases involving an exposure and an adverse event is aware, there are two hurdles that plaintiffs should be forced to overcome: general causation and specific causation.

Establishing Causation in the Courtroom – A Basic Introduction

As every lawyer working on personal injury cases involving an exposure and an adverse event is aware, there are two hurdles that plaintiffs should be forced to overcome: general causation and specific causation. While each of these hurdles should have its own burden of proof, the specific nature of that burden has been hotly debated for many years. In this post, I attempt to broadly outline the nature of the evidence that should exist to satisfy each hurdle.

It should be noted that many of the issues I discuss here have been described in great detail in various treatises (see, for example the Reference Manual on Scientific Evidence, published by the Federal Judicial Center – now in its third edition) and web blogs (see, for example, the excellent scientific/legal blog, Tortini published by my friend and colleague Nathan Schachtman). My goal in this post is to introduce these concepts from a general perspective and then build on the individual aspects of each concept in subsequent blog posts. This post is intended to serve as a general introduction and orientation to these concepts for lawyers new to these issues.

General Causation

General causation refers to the notion that one must evaluate whether the exposure of interest is capable of causing the adverse event in question in some people under some circumstances. There has been ongoing debate between the plaintiff bar and the defense bar as to what constitutes legitimate evidence of general causation. Generally speaking (from my experience consulting on these cases), the plaintiff bar tends to rely upon more anecdotal evidence to support the notion of general causation (including case reports, case series, differential diagnosis, and general medical opinion), while the defense bar tends to limit reliance to more rigorous, controlled studies (e.g., randomized trials, controlled epidemiological studies).

At the end of the day, the plaintiffs must provide “reliable” scientific evidence demonstrating that the exposure at issue (e.g., a drug, medical device, dietary supplement, consumer product, chemical, etc.) is capable of causing the event suffered by the plaintiff. The nature of the reliability of the evidence is a great source of contention in these cases. A 1993 Supreme Court decision, referred to as Daubert, specified specific criteria that must be adhered to to make the evidence reliable, including the following broad principles: utilization of an empiricle testing methodology; evidence subjected to the peer review process; a known or knowable error rate; general acceptance of the theory. Regardless of the specific components utilized by a court, the key to a Daubert admissibility standard has to do with the underlying reliability of the scientific evidence offered to support causation.

The burden of proof in a civil action is referred to using a variety of legal standards, all of which boil down to a similar standard that has been summarized in the following ways:

  • a reasonable degree of scientific/medical certainty
  • more probably than not
  • a preponderance of the evidence

The key to establishing general causation is that a scientific assessment must be made (using reliable scientific evidence) as to whether the exposure is capable of causing the injury in question. So, the question is: how do scientists make this determination? While this is a very contentious question, it seems clear (although not incontrovertible) that a few key principles apply, including the following:

  • quantitative data must be statistically significant to avoid the role that chance may play
  • randomized controlled trials offer the best evidence of causation, followed by controlled observational studies (e.g., cohort epidemiological and case control epidemiological studies)
  • case reports and case series are anecdotal by their very nature and cannot be used to reliably establish causation
  • when relying on observational studies (i.e., controlled epidemiological studies), the role of bias and confounding must be addressed and ruled out
  • when relying on observational data (i.e., non-randomized studies), one must adhere to a set of criteria to come to a reliable assessment of causation – the most widely cited criteria are the Bradford Hill criteria (see, for example, Sir Austin Bradford Hill’s address to the Royal Society of Medicine in 1965 as well as this very recent article recounting stories from the original address)

Specific Causation

Specific causation refers to the notion that we must evaluate whether the exposure caused the adverse event or disease state in the specific individual or individuals in the case. As should be clear from the definition, there is a dramatic difference in distinguishing specific causation from general causation. Clearly, just because an exposure is capable of causing an adverse medical event, it does not mean that it did cause that event in a given individual. Indeed, a great many legal cases are predicated on the notion that general causation is accepted, but that specific causation is in dispute.

One of the most contentious issues in establishing specific causation is whether or not evidence of a doubling of the risk is necessary — i.e., whether or not a risk ratio of at least 2.0 has been demonstrated. This view is predicated on the theory that it is not until one gets to a doubling of the risk that the attribution of the cause of the event in an individual can be attributed to the exposure of interest at a level of “more probable than not.” In other words, if one relies on a study showing that exposure to orange juice is associated with hair loss at a risk ratio of 1.5, the likelihood that the orange juice was the cause of hair loss in any given individual is less likely than not. It is not until one gets to a risk ratio of 2.0 or above that one can support a “more likely than not” argument. Nathan Schachtman has written extensively (and quite lucidly) on his blog about these issues (see, for example, this post and this one).

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