Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning

There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of meta-algorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the Conditional Average Treatment Effect (CATE) function. Meta-algorithms build on base algorithms---such as Random Forests (RF), Bayesian Additive Regression Trees (BART) or neural networks---to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a new meta-algorithm, the X-learner, that is provably efficient when the number of units in one treatment group is much larger than in the other, and can exploit structural properties of the CATE function. For example, if the CATE function is linear and the response functions in treatment and control are Lipschitz continuous, the X-learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X-learner that use RF and BART as base learners. In extensive simulation studies, the X-learner performs favorably, although none of the meta-learners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our new X-learner can be used to target treatment regimes and to shed light on underlying mechanisms. A software package is provided that implements our methods

The Relative Performance of Targeted Maximum Likelihood Estimators

There is an active debate in the literature on censored data about the relative performance of model based maximum likelihood estimators, IPCW-estimators, and a variety of double robust semiparametric efficient estimators. Kang and Schafer (2007) demonstrate the fragility of double robust and IPCW-estimators in a simulation study with positivity violations. They focus on a simple missing data problem with covariates where one desires to estimate the mean of an outcome that is subject to missingness. Responses by Robins et al. (2007), Tsiatis and Davidian (2007), Tan (2007a) and Ridgeway and McCaffrey (2007) further explore the challenges faced by double robust estimators and offer suggestions for improving their stability. In this article, we join the debate by presenting targeted maximum likelihood estimators (TMLEs). We demonstrate that TMLEs that guarantee that the parametric submodel employed by the TMLE-procedure respects the global bounds on the continuous outcomes, are especially suitable for dealing with positivity violations because in addition to being double robust and semiparametric efficient, they are substitution estimators. We demonstrate the practical performance of TMLEs relative to other estimators in the simulations designed by Kang and Schafer (2007) and in modified simulations with even greater estimation challenges

Sufficient Covariate, Propensity Variable and Doubly Robust Estimation

Statistical causal inference from observational studies often requires adjustment for a possibly multi-dimensional variable, where dimension reduction is crucial. The propensity score, first introduced by Rosenbaum and Rubin, is a popular approach to such reduction. We address causal inference within Dawid's decision-theoretic framework, where it is essential to pay attention to sufficient covariates and their properties. We examine the role of a propensity variable in a normal linear model. We investigate both population-based and sample-based linear regressions, with adjustments for a multivariate covariate and for a propensity variable. In addition, we study the augmented inverse probability weighted estimator, involving a combination of a response model and a propensity model. In a linear regression with homoscedasticity, a propensity variable is proved to provide the same estimated causal effect as multivariate adjustment. An estimated propensity variable may, but need not, yield better precision than the true propensity variable. The augmented inverse probability weighted estimator is doubly robust and can improve precision if the propensity model is correctly specified

Extra-short-duration pigeonpea for diversifying wheat-based cropping systems in the sub-tropics

The performance of newly developed extra-short-duration pigeonpea (Cajanus cajan) genotypes and traditional short-duration pigeonpea cultivars was compared in rotation with wheat in on-farm trials conducted in 1996–97 and 1997–98 in Sonepat (28° N) district in Haryana, and in 1996–97 at Ludhiana (30° N) district in Punjab, India. At both locations, a wheat crop (Triticum aestivum cv. HD 2329) followed pigeonpea. At Sonepat, an indeterminate extra-short-duration genotype ICPL 88039 matured up to three weeks earlier, yet gave 12% higher yield (1.57 t ha−1) and showed less susceptibility to borer damage than did the short-duration cv. Manak. At Ludhiana, extra-short-duration pigeonpea genotypes, ICPL 88039, ICPL 85010 and AL 201 gave similar grain yields to the short-duration T 21 in spite of maturing three to four weeks earlier. Yields of wheat crops following extra-short-duration genotypes were up to 0.75 t ha−1 greater at Sonepat and up to 1.0 t ha−1 greater at Ludhiana. The results of the study provide empirical evidence that extra-short-duration pigeonpea genotypes could contribute to higher productivity of pigeonpea–wheat rotation systems. Most of the farmers who grew on-farm trials in Sonepat preferred extra-short-duration to short-duration pigeonpea types for their early maturity, bold seed size, and the greater yield of the following wheat crop

PLEXdb: gene expression resources for plants and plant pathogens

PLEXdb (http://www.plexdb.org), in partnership with community databases, supports comparisons of gene expression across multiple plant and pathogen species, promoting individuals and/or consortia to upload genome-scale data sets to contrast them to previously archived data. These analyses facilitate the interpretation of structure, function and regulation of genes in economically important plants. A list of Gene Atlas experiments highlights data sets that give responses across different developmental stages, conditions and tissues. Tools at PLEXdb allow users to perform complex analyses quickly and easily. The Model Genome Interrogator (MGI) tool supports mapping gene lists onto corresponding genes from model plant organisms, including rice and Arabidopsis. MGI predicts homologies, displays gene structures and supporting information for annotated genes and full-length cDNAs. The gene list-processing wizard guides users through PLEXdb functions for creating, analyzing, annotating and managing gene lists. Users can upload their own lists or create them from the output of PLEXdb tools, and then apply diverse higher level analyses, such as ANOVA and clustering. PLEXdb also provides methods for users to track how gene expression changes across many different experiments using the Gene OscilloScope. This tool can identify interesting expression patterns, such as up-regulation under diverse conditions or checking any gene’s suitability as a steady-state control

Case Report Bilateral Vocal Cord Paralysis and Cervicolumbar Radiculopathy as the Presenting Paraneoplastic Manifestations of Small Cell Lung Cancer: A Case Report and Literature Review

Introduction. Bilateral vocal cord paralysis (BVCP) is a potential medical emergency. The Otolaryngologist plays a crucial role in the diagnosis and management of BVCP and must consider a broad differential diagnosis. We present a rare case of BVCP secondary to anti-Hu paraneoplastic syndrome. Case Presentation. A 58-year-old female presented to an Otolaryngology clinic with a history of progressive hoarseness and dysphagia. Flexible nasolaryngoscopy demonstrated BVCP. Cross-sectional imaging of the brain and vagus nerves was negative. An antiparaneoplastic antibody panel was positive for anti-Hu antibodies. This led to an endobronchial biopsy of a paratracheal lymph node, which confirmed the diagnosis of small cell lung cancer. Conclusion. Paraneoplastic neuropathy is a rare cause of BVCP and should be considered when more common pathologies are ruled out. This is the second reported case of BVCP as a presenting symptom of paraneoplastic syndrome secondary to small cell lung cancer

Temperature-mediated transition from Dyakonov-Tamm surface waves to surface-plasmon-polariton waves

The effect of changing the temperature on the propagation of electromagnetic surface waves (ESWs), guided by the planar interface of a homogeneous isotropic temperature-sensitive material (namely, InSb) and a temperature-insensitive structurally chiral material (SCM) was numerically investigated in the terahertz frequency regime. As the temperature rises, InSb transforms from a dissipative dielectric material to a \blue{dissipative} plasmonic material. Correspondingly, the ESWs transmute from Dyakonov--Tamm surface waves into surface--plasmon--polariton waves. The effects of the temperature change are clearly observed in the phase speeds, propagation distances, angular existence domains, multiplicity, and spatial profiles of energy flow of the ESWs. Remarkably large propagation distances can be achieved; in such instances the energy of an ESW is confined almost entirely within the SCM. For certain propagation directions, simultaneous excitation of two ESWs with (i) the same phase speeds but different propagation distances or (ii) the same propagation distances but different phase speeds are also indicated by our results