Estimation of Causal Effects with Multiple Treatments: A Review and New Ideas

The propensity score is a common tool for estimating the causal effect of a binary treatment in observational data. In this setting, matching, subclassification, imputation, or inverse probability weighting on the propensity score can reduce the initial covariate bias between the treatment and control groups. With more than two treatment options, however, estimation of causal effects requires additional assumptions and techniques, the implementations of which have varied across disciplines. This paper reviews current methods, and it identifies and contrasts the treatment effects that each one estimates. Additionally, we propose possible matching techniques for use with multiple, nominal categorical treatments, and use simulations to show how such algorithms can yield improved covariate similarity between those in the matched sets, relative the pre-matched cohort. To sum, this manuscript provides a synopsis of how to notate and use causal methods for categorical treatments

Lockouts and Player Productivity: Evidence from the National Hockey League

We implement a propensity score matching technique to present the first evidence on the impact of professional sports lockouts on player productivity. In particular, we utilize a unique natural experiment from the 2012-2013 National Hockey League lockout, during which approximately 200 players decided to play overseas, while the rest stayed in North America. We separate players based on their nationality and investigate the effect of playing abroad on postlockout player performance. We find limited evidence of enhanced productivity among European players and no evidence of a benefit or drawback for North American players. Our study contributes to the understanding of lockouts in professional sports and the general discussion of labor disputes and worker productivity

Biased Impartiality Among National Hockey League Referees

This paper builds an economic model of referee behavior in the National Hockey League using period-specific, in-game data. Recognizing that referees are influenced by a desire for perceived fairness, this model isolates situations where a referee is more likely to call a penalty on one team. While prior research has focused on a systematic bias in favor of the home team, we find that referee bias also depends upon game-specific conditions that incentivize an evening of penalty calls. Refereeing games in this fashion maintains the integrity of the game, thus benefiting spectator perceptions and opportunities for financial returns

Estimation of Causal Effects with Multiple Treatments: A Review and New Ideas

The propensity score is a common tool for estimating the causal effect of a binary treatment in observational data. In this setting, matching, subclassification, imputation, or inverse probability weighting on the propensity score can reduce the initial covariate bias between the treatment and control groups. With more than two treatment options, however, estimation of causal effects requires additional assumptions and techniques, the implementations of which have varied across disciplines. This paper reviews current methods, and it identifies and contrasts the treatment effects that each one estimates. Additionally, we propose possible matching techniques for use with multiple, nominal categorical treatments, and use simulations to show how such algorithms can yield improved covariate similarity between those in the matched sets, relative the pre-matched cohort. To sum, this manuscript provides a synopsis of how to notate and use causal methods for categorical treatments

Consistency, Accuracy, and Fairness: A Study of Discretionary Penalties in the NFL

Prior studies of referee behavior focus on identifying a bias in when certain calls are made [Kovash, Kenneth, & Levitt, Steven (2009). Professionals do not play minimax: evidence from Major League Baseball and the National Football League (No. w15347). National Bureau of Economic Research; Rosen, Peter A. and Rick L. Wilson. 2007. An Analysis of the Defense First Strategy in College Football Overtime Games. Journal of Quantitative Analysis in Sports 3(2):1-17; Alamar, Benjamin. 2010. Measuring Risk in NFL Playcalling. Journal of Quantitative Analysis in Sports 6:11.]. We extend this research by evaluating the consistency of specific discretionary penalties in professional football. In doing so, all NFL plays from 2002 to 2012 are considered, isolating the occurrence of holding and pass interference calls. Even after accounting for game and play specific variables, including team characteristics, type of play, and the game\u27s score, we find the likelihood of both penalty types follows a quadratic trend, low at the beginning and ends of the game, but high in the middle. We suggest that these penalties are uniquely called with higher levels of discretion, in an attempt by referees to imply fairness in the flow of the game

Building an NCAA Men’s Basketball Predictive Model and Quantifying Its Success

Computing and machine learning advancements have led to the creation of many cutting-edge predictive algorithms, some of which have been demonstrated to provide more accurate forecasts than traditional statistical tools. In this manuscript, we provide evidence that the combination of modest statistical methods with informative data can meet or exceed the accuracy of more complex models when it comes to predicting the NCAA men\u27s basketball tournament. First, we describe a prediction model that merges the point spreads set by Las Vegas sportsbooks with possession based team efficiency metrics by using logistic regressions. The set of probabilities generated from this model most accurately predicted the 2014 tournament, relative to approximately 400 competing submissions, as judged by the log loss function. Next, we attempt to quantify the degree to which luck played a role in the success of this model by simulating tournament outcomes under different sets of true underlying game probabilities. We estimate that under the most optimistic of game probability scenarios, our entry had roughly a 12% chance of outscoring all competing submissions and just less than a 50% chance of finishing with one of the ten best scores

Stacking-induced fluorescence increase reveals allosteric interactions through DNA

From gene expression to nanotechnology, understanding and controlling DNA requires a detailed knowledge of its higher order structure and dynamics. Here we take advantage of the environment-sensitive photoisomerization of cyanine dyes to probe local and global changes in DNA structure. We report that a covalently attached Cy3 dye undergoes strong enhancement of fluorescence intensity and lifetime when stacked in a nick, gap or overhang region in duplex DNA. This is used to probe hybridization dynamics of a DNA hairpin down to the single-molecule level. We also show that varying the position of a single abasic site up to 20 base pairs away modulates the dye–DNA interaction, indicative of through-backbone allosteric interactions. The phenomenon of stacking-induced fluorescence increase (SIFI) should find widespread use in the study of the structure, dynamics and reactivity of nucleic acids