239 research outputs found
Understanding the Past: Statistical Analysis of Causal Attribution
Would the third-wave democracies have been democratized without prior modernization? What proportion of the past militarized disputes between nondemocracies would have been prevented had those dyads been democratic? Although political scientists often ask these questions of causal attribution, existing quantitative methods fail to address them. This article proposes an alternative statistical methodology based on the widely accepted counterfactual framework of causal inference. The contribution of this article is threefold. First, it clarifies differences between causal attribution and causal effects by specifying the type of research questions to which each quantity is relevant. Second, it provides a clear resolution of the long-standing methodological debate on “selection on the dependent variable.” Third, the article derives new nonparametric identification results, showing that the complier probability of causal attribution can be identified using an instrumental variable. The proposed framework is illustrated via empirical examples from three subfields of political science
Identification, Inference and Sensitivity Analysis for Causal Mediation Effects
Causal mediation analysis is routinely conducted by applied researchers in a
variety of disciplines. The goal of such an analysis is to investigate
alternative causal mechanisms by examining the roles of intermediate variables
that lie in the causal paths between the treatment and outcome variables. In
this paper we first prove that under a particular version of sequential
ignorability assumption, the average causal mediation effect (ACME) is
nonparametrically identified. We compare our identification assumption with
those proposed in the literature. Some practical implications of our
identification result are also discussed. In particular, the popular estimator
based on the linear structural equation model (LSEM) can be interpreted as an
ACME estimator once additional parametric assumptions are made. We show that
these assumptions can easily be relaxed within and outside of the LSEM
framework and propose simple nonparametric estimation strategies. Second, and
perhaps most importantly, we propose a new sensitivity analysis that can be
easily implemented by applied researchers within the LSEM framework. Like the
existing identifying assumptions, the proposed sequential ignorability
assumption may be too strong in many applied settings. Thus, sensitivity
analysis is essential in order to examine the robustness of empirical findings
to the possible existence of an unmeasured confounder. Finally, we apply the
proposed methods to a randomized experiment from political psychology. We also
make easy-to-use software available to implement the proposed methods.Comment: Published in at http://dx.doi.org/10.1214/10-STS321 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
polarization vs. anomalies in the leptoquark models
Polarization measurements in are
useful to check consistency in new physics explanations for the and
anomalies. In this paper, we investigate the and
polarizations and focus on the new physics contributions to the fraction
of a longitudinal polarization (), which is
recently measured by the Belle collaboration , in model-independent manner and in each single leptoquark model (, and ) that can naturally explain the
anomalies. It is found that severely restricts deviation from the Standard Model (SM) prediction of
in the leptoquark models:
[0.43, 0.44], [0.42, 0.48], and [0.43, 0.47] are predicted as a range of
for the , , and leptoquark
models, respectively, where the current data of is satisfied
at level. It is also shown that the polarization observables
can much deviate from the SM predictions. The Belle II experiment, therefore,
can check such correlations between and the polarization
observables, and discriminate among the leptoquark models.Comment: 24 pages, 3 figures, 1 table; references added, version published in
JHE
IDENTIFYING MECHANISMS BEHIND POLICY INTERVENTIONS VIA CAUSAL MEDIATION ANALYSIS
Causal analysis in program evaluation has primarily focused on the question about whether or not a program, or package of policies, has an impact on the targeted outcome of interest. However, it is often of scientific and practical importance to also explain why such impacts occur. In this paper, we introduce causal mediation analysis, a statistical framework for analyzing causal mechanisms that has become increasingly popular in social and medical sciences in recent years. The framework enables us to show exactly what assumptions are sufficient for identifying causal mediation effects for the mechanisms of interest, derive a general algorithm for estimating such mechanism-specific effects, and formulate a sensitivity analysis for the violation of those identification assumptions. We also discuss an extension of the framework to analyze causal mechanisms in the presence of treatment noncompliance, a common problem in randomized evaluation studies. The methods are illustrated via applications to two intervention studies on pre-school classes and job-training workshops
Gluino-mediated electroweak penguin with flavor-violating trilinear couplings
In light of a discrepancy of the direct violation in
decays, , we investigate gluino contributions to
the electroweak penguin, where flavor violations are induced by squark
trilinear couplings. Top-Yukawa contributions to observables are
taken into account, and vacuum stability conditions are evaluated in detail. It
is found that this scenario can explain the discrepancy of
for the squark mass smaller than 5.6 TeV. We also
show that the gluino contributions can amplify , and . Such large effects could be measured in future
experiments.Comment: 30 pages, 8 figures; references added, version published in JHE
Persuading the enemy: estimating the persuasive effects of partisan media with the preference-incorporating choice and assignment design
Does media choice cause polarization, or merely reflect it? We investigate a critical aspect of this puzzle: how partisan media contribute to attitude polarization among different groups of media consumers. We implement a new experimental design, called the Preference-Incorporating
Choice and Assignment (PICA) design, that incorporates both free choice and forced exposure. We estimate jointly the degree of polarization caused by selective exposure and the persuasive effect of partisan media. Our design also enables us to conduct sensitivity analyses accounting
for discrepancies between stated preferences and actual choice, a potential source of bias ignored in previous studies using similar designs. We find that partisan media can polarize both its regular consumers and inadvertent audiences who would otherwise not consume it, but
ideologically-opposing media potentially also can ameliorate existing polarization between consumers. Taken together, these results deepen our understanding of when and how media polarize individuals.Accepted manuscrip
Revisiting kaon physics in general Z scenario
New physics contributions to the Z penguin are revisited in the light of the recently-reported discrepancy of the direct CP violation in K→ππ. Interference effects between the standard model and new physics contributions to
ΔS=2 observables are taken into account. Although the effects are overlooked in the literature, they make experimental bounds significantly severer. It is shown that the new physics contributions must be tuned to enhance B(KL→π0νν¯), if the discrepancy of the direct CP violation is explained with satisfying the experimental constraints. The branching ratio can be as large as 6×10−10 when the contributions are tuned at the 10% level
Revisiting kaon physics in general Z scenario
New physics contributions to the Z penguin are revisited in the light of the recently-reported discrepancy of the direct CP violation in K→ππ. Interference effects between the standard model and new physics contributions to
ΔS=2 observables are taken into account. Although the effects are overlooked in the literature, they make experimental bounds significantly severer. It is shown that the new physics contributions must be tuned to enhance B(KL→π0νν¯), if the discrepancy of the direct CP violation is explained with satisfying the experimental constraints. The branching ratio can be as large as 6×10−10 when the contributions are tuned at the 10% level
Probing SUSY effects in
We explore supersymmetric contributions to the decay
, in light of current experimental data. The
Standard Model (SM) predicts
. We find that
contributions arising from flavour violating Higgs penguins can enhance the
branching fraction up to within different scenarios
of the Minimal Supersymmetric Standard Model (MSSM), as well as suppress it
down to . Regions with fine-tuned parameters can
bring the branching fraction up to the current experimental upper bound,
. The mass degeneracy of the heavy Higgs bosons in MSSM
induces correlations between and
. Predictions for the asymmetry
in decays in the context of MSSM are also given, and
can be up to eight times bigger than in the SM.Comment: 36 pages, 31 fig
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