arXiv subjects

Youjin Lee

Publications and source records attributed to Youjin Lee.

35 records · Page 2Linked to original sources

arXiv2024

Imaging thermally fluctuating Nèel vectors in van der Waals antiferromagnet NiPS3

Studying antiferromagnetic domains is essential for fundamental physics and potential spintronics applications. Despite its importance, few systematic studies have been performed on van der Waals (vdW) antiferromagnets (AFMs) domains with high spatial resolutions, and direct probing of the Nèel vectors remains challenging. In this work, we found a multidomain in vdW AFM NiPS3, a material extensively investigated for its exotic magnetic exciton. We employed photoemission electron microscopy combined with the X-ray magnetic linear dichroism (XMLD-PEEM) to image the NiPS3's magnetic structure. The nanometer-spatial resolution of XMLD-PEEM allows us to determine local Nèel vector orientations and discover thermally fluctuating Néel vectors that are independent of the crystal symmetry even at 65 K, well below TN of 155 K. We demonstrate a Ni ions' small in-plane orbital moment anisotropy is responsible for the weak magneto-crystalline anisotropy. The observed multidomain's thermal fluctuations may explain the broadening of magnetic exciton peaks at higher temperatures.

cond-mat.mtrl-sci↗

arXiv2024

Giant Linear Dichroism Controlled by Magnetic Field in FePS$_3$

Magnetic-field control of fundamental optical properties is a crucial challenge in the engineering of multifunctional microdevices. Van der Waals (vdW) magnets retaining a magnetic order even in atomically thin layers, offer a promising platform for hosting exotic magneto-optical functionalities owing to their strong spin-charge coupling. Here, we demonstrate that a giant optical anisotropy can be controlled by magnetic fields in the vdW magnet FePS$_3$. The giant linear dichroism ($\sim$11%), observed below $T_{\text{N}}\!\sim\!120$ K, is nearly fully suppressed in a wide energy range from 1.6 to 2.0 eV, following the collapse of the zigzag magnetic order above 40 T. This remarkable phenomenon can be explained as a result of symmetry changes due to the spin order, enabling minority electrons of Fe$^{2+}$ to hop in a honeycomb lattice. The modification of spin-order symmetry by external fields provides a novel route for controllable anisotropic optical micro-devices.

cond-mat.mtrl-sci↗

arXiv2024

Multiply Robust Difference-in-Differences Estimation of Causal Effect Curves for Continuous Exposures

Researchers commonly use difference-in-differences (DiD) designs to evaluate public policy interventions. While methods exist for estimating effects in the context of binary interventions, policies often result in varied exposures across regions implementing the policy. Yet, existing approaches for incorporating continuous exposures face substantial limitations in addressing confounding variables associated with intervention status, exposure levels, and outcome trends. These limitations significantly constrain policymakers' ability to fully comprehend policy impacts and design future interventions. In this work, we propose new estimators for causal effect curves within the DiD framework, accounting for multiple sources of confounding. Our approach accommodates misspecification of a subset of treatment, exposure, and outcome models while avoiding any parametric assumptions on the effect curve. We present the statistical properties of the proposed methods and illustrate their application through simulations and a study investigating the heterogeneous effects of a nutritional excise tax under different levels of accessibility to cross-border shopping.

stat.ME↗

arXiv2023

RobustIV and controlfunctionIV: Causal Inference for Linear and Nonlinear Models with Invalid Instrumental Variables

We present R software packages RobustIV and controlfunctionIV for causal inference with possibly invalid instrumental variables. RobustIV focuses on the linear outcome model. It implements the two-stage hard thresholding method to select valid instrumental variables from a set of candidate instrumental variables and make inferences for the causal effect in both low- and high-dimensional settings. Furthermore, RobustIV implements the high-dimensional endogeneity test and the searching and sampling method, a uniformly valid inference method robust to errors in instrumental variable selection. controlfunctionIV considers the nonlinear outcome model and makes inferences about the causal effect based on the control function method. Our packages are demonstrated using two publicly available economic data sets together with applications to the Framingham Heart Study.

stat.ME↗

arXiv2023

Policy effect evaluation under counterfactual neighborhood interventions in the presence of spillover

Policy interventions can spill over to units of a population that are not directly exposed to the policy but are geographically close to the units receiving the intervention. In recent work, investigations of spillover effects on neighboring regions have focused on estimating the average treatment effect of a particular policy in an observed setting. Our research question broadens this scope by asking what policy consequences would the treated units have experienced under hypothetical exposure settings. When we only observe treated unit(s) surrounded by controls -- as is common when a policy intervention is implemented in a single city or state -- this effect inquires about the policy effects under a counterfactual neighborhood policy status that we do not, in actuality, observe. In this work, we extend difference-in-differences (DiD) approaches to spillover settings and develop identification conditions required to evaluate policy effects in counterfactual treatment scenarios. These causal quantities are policy-relevant for designing effective policies for populations subject to various neighborhood statuses. We develop doubly robust estimators and use extensive numerical experiments to examine their performance under heterogeneous spillover effects. We apply our proposed method to investigate the effect of the Philadelphia beverage tax on unit sales.

stat.ME↗

arXiv2023

Estimation of Policy-Relevant Causal Effects in the Presence of Interference with an Application to the Philadelphia Beverage Tax

To comprehensively evaluate a public policy intervention, researchers must consider the effects of the policy not just on the implementing region, but also nearby, indirectly-affected regions. For example, an excise tax on sweetened beverages in Philadelphia was shown to not only be associated with a decrease in volume sales of taxed beverages in Philadelphia, but also an increase in sales in bordering counties not subject to the tax. The latter association may be explained by cross-border shopping behaviors of Philadelphia residents and indicate a causal effect of the tax on nearby regions, which may offset the total effect of the intervention. To estimate causal effects in this setting, we extend difference-in-differences methodology to account for such interference between regions and adjust for potential confounding present in quasi-experimental evaluations. Our doubly robust estimators for the average treatment effect on the treated and neighboring control relax standard assumptions on interference and model specification. We apply these methods to evaluate the change in volume sales of taxed beverages in 231 Philadelphia and bordering county stores due to the Philadelphia beverage tax. We also use our methods to explore the heterogeneity of effects across geographic features.

stat.ME↗

arXiv2022

Giant magnetic anisotropy in the atomically thin van der Waals antiferromagnet FePS3

Van der Waals (vdW) magnets are an ideal platform for tailoring two-dimensional (2D) magnetism with immense potential for spintronics applications and are intensively investigated. However, little is known about the microscopic origin of magnetic order in these antiferromagnetic systems. We used X-ray photoemission electron microscopy to address the electronic and magnetic properties of the vdW antiferromagnet FePS3 down to the monolayer. Our experiments reveal a giant out-of-plane magnetic anisotropy of 22 meV per Fe ion, accompanied by unquenched magnetic orbital moments. Moreover, our calculations suggest that the Ising magnetism in FePS3 is a visible manifestation of spin-orbit entanglement of the Fe 3d electron system.

cond-mat.mes-hall↗

arXiv2021

Multiferroic-enabled magnetic exciton in 2D quantum entangled van der Waals antiferromagnet NiI2

Matter-light interaction is at the center of diverse research fields from quantum optics to condensed matter physics, opening new fields like laser physics. A magnetic exciton is one such rare example found in magnetic insulators. However, it is relatively rare to observe that external variables control matter-light interaction. Here, we report that the broken inversion symmetry of multiferroicity can act as an external knob enabling the magnetic exciton in van der Waals antiferromagnet NiI2. We further discover that this magnetic exciton arises from a transition between Zhang-Rice-triplet and Zhang-Rice-singlet's fundamentally quantum entangled states. This quantum entanglement produces an ultra-sharp optical exciton peak at 1.384 eV with a 5 meV linewidth. Our work demonstrates that NiI2 is two-dimensional magnetically ordered with an intrinsically quantum entangled ground state.

cond-mat.str-el↗

arXiv2021

Possible persistence of multiferroic order down to bilayer limit of van der Waals material NiI$_{2}$

Realizing a state of matter in two dimensions has repeatedly proven a novel route of discovering new physical phenomena. Van der Waals (vdW) materials have been at the center of these now extensive research activities. They offer a natural way of producing a monolayer of matter simply by mechanical exfoliation. This work demonstrates that the possible multiferroic state with coexisting antiferromagnetic and ferroelectric orders possibly persists down to the bilayer flake of NiI$_{2}$. By exploiting the optical second-harmonic generation technique, both magnitude and direction of the ferroelectric order, arising from the cycloidal spin order, are successfully traced. The possible multiferroic state's transition temperature decreases from 58 K for the bulk to about 20 K for the bilayer. Our observation will spur extensive efforts to demonstrate multi-functionality in vdW materials, which have been tried mostly by using heterostructures of singly ferroic ones until now.

cond-mat.mtrl-sci↗

arXiv2020

Partially Pooled Propensity Score Models for Average Treatment Effect Estimation with Multilevel Data

Causal inference analyses often use existing observational data, which in many cases has some clustering of individuals. In this paper we discuss propensity score weighting methods in a multilevel setting where within clusters individuals share unmeasured confounders that are related to treatment assignment and the potential outcomes. We focus in particular on settings where models with fixed cluster effects are either not feasible or not useful due to the presence of a large number of small clusters. We found, both through numerical experiments and theoretical derivations, that a strategy of grouping clusters with similar treatment prevalence and estimating propensity scores within such cluster groups is effective in reducing bias from unmeasured cluster-level covariates under mild conditions on the outcome model. We apply our proposed method in evaluating the effectiveness of center-based pre-school program participation on children's achievement at kindergarten, using the Early Childhood Longitudinal Study, Kindergarten data.

stat.AP↗

arXiv2020

Doubly Robust Nonparametric Instrumental Variable Estimators for Survival Outcomes

Instrumental variable (IV) methods allow us the opportunity to address unmeasured confounding in causal inference. However, most IV methods are only applicable to discrete or continuous outcomes with very few IV methods for censored survival outcomes. In this work we propose nonparametric estimators for the local average treatment effect on survival probabilities under both nonignorable and ignorable censoring. We provide an efficient influence function-based estimator and a simple estimation procedure when the IV is either binary or continuous. The proposed estimators possess double-robustness properties and can easily incorporate nonparametric estimation using machine learning tools. In simulation studies, we demonstrate the flexibility and efficiency of our proposed estimators under various plausible scenarios. We apply our method to the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial for estimating the causal effect of screening on survival probabilities and investigate the causal contrasts between the two interventions under different censoring assumptions.

stat.ME↗

arXiv2020

Observation of plateau-like magnetoresistance in twisted Fe3GeTe2/Fe3GeTe2 junction

Controlling the stacking of van der Waals (vdW) materials is found to produce exciting new findings, since hetero- or homo- structures have added the diverse possibility of assembly and manipulated functionalities. However, so far, the homostructure with a twisted angle based on the magnetic vdW materials remains unexplored. Here, we achieved a twisted magnetic vdW Fe3GeTe2/Fe3GeTe2 junction with broken crystalline symmetry. A clean and metallic vdW junction is evidenced by the temperature-dependent resistance and the linear I-V curve. Unlike the pristine FGT, a plateau-like magnetoresistance (PMR) is observed in the magnetotransport of our homojunction due to the antiparallel magnetic configurations of the two FGT layers. The PMR ratio is found to be ~0.05% and gets monotonically enhanced as temperature decreases like a metallic giant magnetoresistance (GMR). Such a tiny PMR ratio is at least three orders of magnitude smaller than the tunneling magnetoresistance (TMR) ratio, justifying our clean metallic junction without a spacer. Our findings demonstrate the feasibility of the controllable homostructure and shed light on future spintronics using magnetic vdW materials.

cond-mat.mtrl-sci↗

arXiv2020

Two Robust Tools for Inference about Causal Effects with Invalid Instruments

Instrumental variables have been widely used to estimate the causal effect of a treatment on an outcome. Existing confidence intervals for causal effects based on instrumental variables assume that all of the putative instrumental variables are valid; a valid instrumental variable is a variable that affects the outcome only by affecting the treatment and is not related to unmeasured confounders. However, in practice, some of the putative instrumental variables are likely to be invalid. This paper presents two tools to conduct valid inference and tests in the presence of invalid instruments. First, we propose a simple and general approach to construct confidence intervals based on taking unions of well-known confidence intervals. Second, we propose a novel test for the null causal effect based on a collider bias. Our two proposals, especially when fused together, outperform traditional instrumental variable confidence intervals when invalid instruments are present, and can also be used as a sensitivity analysis when there is concern that instrumental variables assumptions are violated. The new approach is applied to a Mendelian randomization study on the causal effect of low-density lipoprotein on the incidence of cardiovascular diseases.

stat.ME↗

arXiv2020

Causal inference, social networks, and chain graphs

Traditionally, statistical and causal inference on human subjects rely on the assumption that individuals are independently affected by treatments or exposures. However, recently there has been increasing interest in settings, such as social networks, where individuals may interact with one another such that treatments may spill over from the treated individual to their social contacts and outcomes may be contagious. Existing models proposed for causal inference using observational data from networks of interacting individuals have two major shortcomings. First, they often require a level of granularity in the data that is practically infeasible to collect in most settings, and second, the models are high-dimensional and often too big to fit to the available data. In this paper we illustrate and justify a parsimonious parameterization for network data with interference and contagion. Our parameterization corresponds to a particular family of graphical models known as chain graphs. We argue that, in some settings, chain graph models approximate the marginal distribution of a snapshot of a longitudinal data generating process on interacting units. We illustrate the use of chain graphs for causal inference about collective decision making in social networks using data from U.S. Supreme Court decisions between 1994 and 2004 and in simulations.

stat.ME↗

arXiv2020

Network Dependence Can Lead to Spurious Associations and Invalid Inference

Researchers across the health and social sciences generally assume that observations are independent, even while relying on convenience samples that draw subjects from one or a small number of communities, schools, hospitals, etc. A paradigmatic example of this is the Framingham Heart Study (FHS). Many of the limitations of such samples are well-known, but the issue of statistical dependence due to social network ties has not previously been addressed. We show that, along with anticonservative variance estimation, this can result in spurious associations due to network dependence. Using a statistical test that we adapted from one developed for spatial autocorrelation, we test for network dependence in several of the thousands of influential papers that have been published using FHS data. Results suggest that some of the many decades of research on coronary heart disease, other health outcomes, and peer influence using FHS data may suffer from spurious associations, error-prone point estimates, and anticonservative inference due to unacknowledged network dependence. These issues are not unique to the FHS; as researchers in psychology, medicine, and beyond grapple with replication failures, this unacknowledged source of invalid statistical inference should be part of the conversation.

stat.AP↗

arXiv2020

Testing for Network and Spatial Autocorrelation

Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties for testing for the presence of spatial autocorrelation in continuous variables. In this paper we propose two contributions to the literature on tests for autocorrelation. First, we propose a new test for autocorrelation in categorical variables. While some methods currently exist for assessing spatial autocorrelation in categorical variables, the most popular method is unwieldy, somewhat ad hoc, and fails to provide grounds for a single omnibus test. Second, we discuss the importance of testing for autocorrelation in data sampled from the nodes of a network, motivated by social network applications. We demonstrate that our proposed statistic for categorical variables can both be used in the spatial and network setting.

stat.AP↗

arXiv2019

Network Dependence Testing via Diffusion Maps and Distance-Based Correlations

Deciphering the associations between network connectivity and nodal attributes is one of the core problems in network science. The dependency structure and high-dimensionality of networks pose unique challenges to traditional dependency tests in terms of theoretical guarantees and empirical performance. We propose an approach to test network dependence via diffusion maps and distance-based correlations. We prove that the new method yields a consistent test statistic under mild distributional assumptions on the graph structure, and demonstrate that it is able to efficiently identify the most informative graph embedding with respect to the diffusion time. The methodology is illustrated on both simulated and real data.

stat.ME↗