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In designing and evaluating public policies, policymakers and researchers often hypothesize about the mechanisms through which a policy may affect a population and aim to assess these mechanisms in practice. For example, when studying an excise tax on sweetened beverages, researchers might explore how cross-border shopping, economic competition, and store-level price changes differentially affect store sales. However, many policy evaluation designs, including the difference-in-differences (DiD) approach, traditionally target the average effect of the intervention rather than the underlying mechanisms. Extensions of these approaches to evaluate policy mechanisms often involve exploratory subgroup analyses or outcome models parameterized by mechanism-specific variables. However, neither approach studies the mechanisms within a causal framework, limiting the analysis to associative relationships between mechanisms and outcomes, which may be confounded by differences among sub-populations exposed to varying levels of the mechanisms. Therefore, rigorous mechanism evaluation requires robust techniques to adjust for confounding and accommodate the interconnected relationship between stores within competitive economic landscapes. In this paper, we present a framework for evaluating policy mechanisms by studying Philadelphia beverage tax. Our approach builds on recent advancements in causal effect curve estimators under DiD designs, offering tools and insights for assessing policy mechanisms complicated by confounding and network interference.

Difference-in-differences (DiD) is the most popular observational causal inference method in health policy, employed to evaluate the real-world impact of policies and programs. To estimate treatment effects, DiD relies on the "parallel trends assumption", that on average treatment and comparison groups would have had parallel trajectories in the absence of an intervention. Historically, DiD has been considered broadly applicable and straightforward to implement, but recent years have seen rapid advancements in DiD methods. This paper reviews and synthesizes these innovations for medical and health policy researchers. We focus on four topics: (1) assessing the parallel trends assumption in health policy contexts; (2) relaxing the parallel trends assumption when appropriate; (3) employing estimators to account for staggered treatment timing; and (4) conducting robust inference for analyses in which normal-based clustered standard errors are inappropriate. For each, we explain challenges and common pitfalls in traditional DiD and modern methods available to address these issues.

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 N\`eel 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 N\`eel vector orientations and discover thermally fluctuating N\'eel 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.

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., Comment: 5 pages, 4 figures

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.

Prior work applying semiparametric theory to causal inference has primarily focused on deriving estimators that exhibit statistical robustness under a prespecified causal model that permits identification of a desired causal parameter. However, a fundamental challenge is correct specification of such a model, which usually involves making untestable assumptions. Evidence factors is an approach to combining hypothesis tests of a common causal null hypothesis under two or more candidate causal models. Under certain conditions, this yields a test that is valid if at least one of the underlying models is correct, which is a form of causal robustness. We propose a method of combining semiparametric theory with evidence factors. We develop a causal null hypothesis test based on joint asymptotic normality of K asymptotically linear semiparametric estimators, where each estimator is based on a distinct identifying functional derived from each of K candidate causal models. We show that this test provides both statistical and causal robustness in the sense that it is valid if at least one of the K proposed causal models is correct, while also allowing for slower than parametric rates of convergence in estimating nuisance functions. We demonstrate the effectiveness of our method via simulations and applications to the Framingham Heart Study and Wisconsin Longitudinal Study.

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.

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.

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.

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., Comment: 26 pages, 10 figures. Accepted for publication in Advanced Materials

Robust multi-level spin memory with the ability to write information electrically is a long-sought capability in spintronics, with great promise for applications. Here we achieve nonvolatile and highly energy-efficient magnetization switching in a single-material device formed of van-der-Waals topological ferromagnet Fe3GeTe2, whose magnetic information can be readily controlled by a tiny current. Furthermore, the switching current density and power dissipation are about 400 and 4000 times smaller than those of the existing spin-orbit-torque magnetic random access memory based on conventional magnet/heavy-metal systems. Most importantly, we also demonstrate multi-level states, switched by electrical current, which can dramatically enhance the information capacity density and reduce computing costs. Thus, our observations combine both high energy efficiency and large information capacity density in one device, showcasing the potential applications of the emerging field of van-der-Waals magnets in the field of spin memory and spintronics., Comment: Accepted by Advanced Functional Materials; 28 pages, 5 main figures, 4 supporting figures

Controlling magnetic states by a small current is essential for the next-generation of energy-efficient spintronic devices. However, it invariably requires considerable energy to change a magnetic ground state of intrinsically quantum nature governed by fundamental Hamiltonian, once stabilized below a phase transition temperature. We report that surprisingly an in-plane current can tune the magnetic state of nm-thin van der Waals ferromagnet Fe3GeTe2 from a hard magnetic state to a soft magnetic state. It is the direct demonstration of the current-induced substantial reduction of the coercive field. This surprising finding is possible because the in-plane current produces a highly unusual type of gigantic spin-orbit torque for Fe3GeTe2. And we further demonstrate a working model of a new nonvolatile magnetic memory based on the principle of our discovery in Fe3GeTe2, controlled by a tiny current. Our findings open up a new window of exciting opportunities for magnetic van der Waals materials with potentially huge impacts on the future development of spintronic and magnetic memory., Comment: 61 pages, 4 main figures, 14 supporting figures

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.

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., Comment: 10 pages, 4 figures, accepted for publication in Journal of Applied physics

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.

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.

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.

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.

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.

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.

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.