Little is known about how to generate plausible new scientific ideas. So it is noteworthy that 12 years of self-experimentation led to the discovery of several surprising cause-effect relationships and suggested a new theory of weight control, an unusually high rate of new ideas. The cause-effect relationships were: (1) Seeing faces in the morning on television decreased mood in the evening (>10 hrs later) and improved mood the next day (>24 hrs later), yet had no detectable effect before that (0–10 (...) hrs later). The effect was strongest if the faces were life-sized and at a conversational distance. Travel across time zones reduced the effect for a few weeks. (2) Standing 8 hours per day reduced early awakening and made sleep more restorative, even though more standing was associated with less sleep. (3) Morning light (1 hr/day) reduced early awakening and made sleep more restorative. (4) Breakfast increased early awakening. (5) Standing and morning light together eliminated colds (upper respiratory tract infections) for more than 5 years. (6) Drinking lots of water, eating low-glycemic-index foods, and eating sushi each caused a modest weight loss. (7) Drinking unflavored fructose water caused a large weight loss that has lasted more than 1 year. While losing weight, hunger was much less than usual. Unflavored sucrose water had a similar effect. The new theory of weight control, which helped discover this effect, assumes that flavors associated with calories raise the body-fat set point: The stronger the association, the greater the increase. Between meals the set point declines. Self-experimentation lasting months or years seems to be a good way to generate plausible new ideas. Key Words: breakfast; circadian; colds; depression; discovery; fructose; innovation; insomnia; light; obesity; sitting; standing; sugar. (shrink)

The stratum-specific treatment effect function is a random variable giving the average treatment effect (ATE) for a randomly drawn stratum of potential confounders a clinician may use to assign treatment. In addition to the ATE, the variance of the stratum-specific treatment effect function is fundamental in determining the heterogeneity of treatment effect values. We offer a non-parametric plug-in estimator, the targeted maximum likelihood estimator (TMLE) and the cross-validated TMLE (CV-TMLE), to simultaneously estimate both the average and variance of the stratum-specific (...) treatment effect function. The CV-TMLE is preferable because it guarantees asymptotic efficiency under two conditions without needing entropy conditions on the initial fits of the outcome model and treatment mechanism, as required by TMLE. Particularly, in circumstances where data adaptive fitting methods are very important to eliminate bias but hold no guarantee of satisfying the entropy condition, we show that the CV-TMLE sampling distributions maintain normality with a lower mean squared error than TMLE. In addition to verifying the theoretical properties of TMLE and CV-TMLE through simulations, we highlight some of the challenges in estimating the variance of the treatment effect, which lack double robustness and might be biased if the true variance is small and sample size insufficient. (shrink)

Social experimental research commonly employs media to elicit responses from research subjects. This use of media is broadly protected under fair use exemptions to copyright, and creators of content used in experiments are generally not afforded any formal consideration or protections in existing research ethics frameworks. Online social networking sites are an emerging and important setting for social experiments, and in this context the material used to elicit responses is often content produced by other users. This article argues that users (...) may have a reasonable interest in controlling the use of their content in experiments conducted in online social networks. Matters of risk and autonomy in research ethics are explored by analogy to active debates in law over adhesion contracts, moral rights, and the right to be forgotten. The article concludes by considering practical difficulties in identifying and protecting the interests of creators. (shrink)

Causal inference in the empiricalsciences is based on counterfactuals. The mostcommon approach utilizes a statistical model ofpotential outcomes to estimate causal effectsof treatments. On the other hand, one leadingapproach to the study of causation inphilosophical logic has been the analysis ofcausation in terms of counterfactualconditionals. This paper discusses and connectsboth approaches to counterfactual causationfrom philosophy and statistics. Specifically, Ipresent the counterfactual account of causationin terms of Lewis's possible-world semantics,and reformulate the statistical potentialoutcome framework using counterfactualconditionals. This procedure highlights variousproperties and (...) mechanisms of the statisticalmodel. (shrink)

Defining and identifying causal intervention effects for transmissible infectious disease outcomes is challenging because a treatment – such as a vaccine – given to one individual may affect the infection outcomes of others. Epidemiologists have proposed causal estimands to quantify effects of interventions under contagion using a two-person partnership model. These simple conceptual models have helped researchers develop causal estimands relevant to clinical evaluation of vaccine effects. However, many of these partnership models are formulated under structural assumptions that preclude realistic (...) infectious disease transmission dynamics, limiting their conceptual usefulness in defining and identifying causal treatment effects in empirical intervention trials. In this paper, we propose causal intervention effects in two-person partnerships under arbitrary infectious disease transmission dynamics, and give nonparametric identification results showing how effects can be estimated in empirical trials using time-to-infection or binary outcome data. The key insight is that contagion is a causal phenomenon that induces conditional independencies on infection outcomes that can be exploited for the identification of clinically meaningful causal estimands. These new estimands are compared to existing quantities, and results are illustrated using a realistic simulation of an HIV vaccine trial. (shrink)

Recently developed theory using directed graphs permits simple and precise statements about the validity of causal inferences in most cases. Applying this while reading econometric papers can make it easy to understand assumptions that are vague in prose, and to isolate those assumptions that are crucial to support the main causal claims. The method is illustrated here alongside a close reading of the paper that introduced the use of settler mortality to instrument the impact of institutions on economic development. Two (...) causal pathways that invalidate the instrument are found not to be blocked by satisfactory strategies. The estimates in the original paper, and in many that have used the instrument since, should be considered highly suspect. (shrink)