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ABSTRACT We present a lowfrequency (170–200 MHz) search for prompt radio emission associated with the long GRB 210419A using the rapidresponse mode of the Murchison Widefield Array (MWA), triggering observations with the Voltage Capture System for the first time. The MWA began observing GRB 210419A within 89 s of its detection by Swift, enabling us to capture any dispersion delayed signal emitted by this gammaray burst (GRB) for a typical range of redshifts. We conducted a standard single pulse search with a temporal and spectral resolution of $100\, \mu$s and 10 kHz over a broad range of dispersion measures from 1 to $5000\, \text{pc}\, \text{cm}^{3}$, but none were detected. However, fluence upper limits of 77–224 Jy ms derived over a pulse width of 0.5–10 ms and a redshift of 0.6 < z < 4 are some of the most stringent at low radio frequencies. We compared these fluence limits to the GRB jet–interstellar medium interaction model, placing constraints on the fraction of magnetic energy (ϵB ≲ [0.05–0.1]). We also searched for signals during the Xray flaring activity of GRB 210419A on minute timescales in the image domain and found no emission, resulting in an intensity upper limit of $0.57\, \text{Jy}\, \text{beam}^{1}$, corresponding to a constraint ofmore »Free, publiclyaccessible full text available June 21, 2023

Abstract Many short gammaray bursts (GRBs) originate from binary neutron star mergers, and there are several theories that predict the production of coherent, prompt radio signals either prior, during, or shortly following the merger, as well as persistent pulsarlike emission from the spindown of a magnetar remnant. Here we present a low frequency (170–200 MHz) search for coherent radio emission associated with nine short GRBs detected by the Swift and/or Fermi satellites using the Murchison Widefield Array (MWA) rapidresponse observing mode. The MWA began observing these events within 30–60 s of their highenergy detection, enabling us to capture any dispersion delayed signals emitted by short GRBs for a typical range of redshifts. We conducted transient searches at the GRB positions on timescales of 5 s, 30 s, and 2 min, resulting in the most constraining flux density limits on any associated transient of 0.42, 0.29, and 0.084 Jy, respectively. We also searched for dispersed signals at a temporal and spectral resolution of 0.5 s and 1.28 MHz, but none were detected. However, the fluence limit of 80–100 Jy ms derived for GRB 190627A is the most stringent to date for a short GRB. Assuming the formation of a stable magnetarmore »Free, publiclyaccessible full text available January 1, 2023

It is common to quantify causal effects with mean values, which, however, may fail to capture significant distribution differences of the outcome under different treatments. We study the problem of estimating the density of the causal effect of a binary treatment on a continuous outcome given a binary instrumental variable in the presence of covariates. Specifically, we consider the local treatment effect, which measures the effect of treatment among those who comply with the assignment under the assumption of monotonicity (only the ones who were offered the treatment take it). We develop two families of methods for this task, kernelsmoothing and modelbased approximations  the former smoothes the density by convoluting with a smooth kernel function; the latter projects the density onto a finitedimensional density class. For both approaches, we derive double/debiased machine learning (DML) based estimators. We study the asymptotic convergence rates of the estimators and show that they are robust to the biases in nuisance function estimation. We illustrate the proposed methods on synthetic data and a real dataset called 401(k).

Generalizing causal effects from a controlled experiment to settings beyond the particular study population is arguably one of the central tasks found in empirical circles. While a proper design and careful execution of the experiment would support, under mild conditions, the validity of inferences about the population in which the experiment was conducted, two challenges make the extrapolation step to different populations somewhat involved, namely, transportability and sampling selection bias. The former is concerned with disparities in the distributions and causal mechanisms between the domain (i.e., settings, population, environment) where the experiment is conducted and where the inferences are intended; the latter with distortions in the sample’s proportions due to preferential selection of units into the study. In this paper, we investigate the assumptions and machinery necessary for using covariate adjustment to correct for the biases generated by both of these problems, and generalize experimental data to infer causal effects in a new domain. We derive complete graphical conditions to determine if a set of covariates is admissible for adjustment in this new setting. Building on the graphical characterization, we develop an efficient algorithm that enumerates all possible admissible sets with polytime delay guarantee; this can be useful for whenmore »

Causeandeffect relations are one of the most valuable types of knowledge sought after throughout the datadriven sciences since they translate into stable and generalizable explanations as well as efficient and robust decisionmaking capabilities. Inferring these relations from data, however, is a challenging task. Two of the most common barriers to this goal are known as confounding and selection biases. The former stems from the systematic bias introduced during the treatment assignment, while the latter comes from the systematic bias during the collection of units into the sample. In this paper, we consider the problem of identifiability of causal effects when both confounding and selection biases are simultaneously present. We first investigate the problem of identifiability when all the available data is biased. We prove that the algorithm proposed by [Bareinboim and Tian, 2015] is, in fact, complete, namely, whenever the algorithm returns a failure condition, no identifiability claim about the causal relation can be made by any other method. We then generalize this setting to when, in addition to the biased data, another piece of external data is available, without bias. It may be the case that a subset of the covariates could be measured without bias (e.g., from census).more »

Selection and confounding biases are the two most common impediments to the applicability of causal inference methods in largescale settings. We generalize the notion of backdoor adjustment to account for both biases and leverage external data that may be available without selection bias (e.g., data from census). We introduce the notion of adjustment pair and present complete graphical conditions for identifying causal effects by adjustment. We further design an algorithm for listing all admissible adjustment pairs in polynomial delay, which is useful for researchers interested in evaluating certain properties of some admissible pairs but not all (common properties include cost, variance, and feasibility to measure). Finally, we describe a statistical estimation procedure that can be performed once a set is known to be admissible, which entails different challenges in terms of finite samples.

Free, publiclyaccessible full text available December 1, 2022