Partial Identification Research Articles

The impact of coarsening an exposure on partial identifiability in instrumental variable settings.

In instrumental variable (IV) settings, such as imperfect randomized trials and observational studies with Mendelian randomization, one may encounter a continuous exposure, the causal effect of which is not of true interest. Instead, scientific interest may lie in a coarsened version of this exposure. Although there is a lengthy literature on the impact of coarsening of an exposure with several works focusing specifically on IV settings, all methods proposed in this literature require parametric assumptions. Instead, just as in the standard IV setting, one can consider partial identification via bounds making no parametric assumptions. This was first pointed out in Alexander Balke's PhD dissertation. We extend and clarify his work and derive novel bounds in several settings, including for a three-level IV, which will most likely be the case in Mendelian randomization. We demonstrate our findings in two real data examples, a randomized trial for peanut allergy in infants and a Mendelian randomization setting investigating the effect of homocysteine on cardiovascular disease.

Partial Identification of the Effect of Retirement on Mental Health with Panel Data.

Partial Identification of the Effect of Retirement on Mental Health with Panel Data.

Brassinosteroids mediate arbuscular mycorrhizal symbiosis through multiple potential pathways and partial identification in tomato

Currently, little is known regarding the specific processes through which brassinosteroids (BR) affect arbuscular mycorrhizal (AM) symbiosis. Understanding this relationship is vital for advancing plant physiology and agricultural applications. In this study, we aimed to elucidate the regulatory mechanisms of BR in AM symbiosis. According to the log2 fold change-value and adjP-value, we integrated the common differentially expressed genes (DEGs) in maize (Zea mays L.) treated with BR and AM, Arabidopsis (Arabidopsis thaliana) mutants deficient in BR receptors, and tomato (Solanum lycopersicum) plants inoculated with AM fungi. In addition, we characterized the symbiotic performance of tomato plants with BR receptor defects and overexpression. The results indicated that the common differential genes induced by BR and AM were involved in metabolic processes, such as cell wall modification, cytoskeleton remodeling, auxin and ethylene signaling, photosynthesis, mineral nutrient transport, and stress defense. Specifically, these include the BR1 gene, which modifies the cell wall. However, the fungal colonization rate of BR receptor-deficient tomato plants was significantly reduced, and the total phosphorus concentration was increased. Conversely, the performance of the overexpressing tomato transformation plants demonstrated a significant contrast. Additionally, the mild rescue of mycorrhizal attenuation in mutants treated with exogenous BR suggests the possibility of direct feedback from BR synthesis to AM. Notably, the cell wall modification gene (SlBR1) and calcium spike gene (SlIPD3) were induced by both BR and AM, suggesting that BR may influence cell penetration during the early stages of AM colonization. Synthesis: Our results demonstrated that BR positively regulates AM symbiosis through multiple pathways. These findings pave the way for future research, including isolation of the individual contributions of each pathway to this complex process and exploration of possible agricultural applications.

Does college selectivity reduce obesity? A partial identification approach.

We use data from the National Longitudinal Study of Adolescent to Adult Health to investigate whether the quality of tertiary education -measured by college selectivity-causally affects obesity prevalence in the medium run (by age 24-34) and in the longer run (about 10years later). We use partial identification methods, which allow us, while relying on weak assumptions, to overcome the potential endogeneity of college selectivity as well as the potential violation of the stable unit treatment value assumption due to students interacting with each other, and to obtain informative identification regions for the average treatment effect of college selectivity on obesity. We find that attending a more selective college causally reduces obesity, both in the medium and in the longer run. We provide evidence that the mechanisms through which the impact of college selectivity on obesity operates include an increase in income, a reduction in physical inactivity and in the consumption of fast food and sweetened drinks.

Measuring and Mitigating Racial Disparities in Tax Audits

Abstract Tax authorities around the world rely on audits to detect underreported tax liabilities and to verify that taxpayers qualify for the benefits they claim. We study differences in Internal Revenue Service audit rates between Black and non-Black taxpayers. Because neither we nor the IRS observe taxpayer race, we propose and use a novel partial identification strategy to estimate these differences. Despite race-blind audit selection, we find that Black taxpayers are audited at 2.9 to 4.7 times the rate of non-Black taxpayers. An important driver of the disparity is differing audit rates by race among taxpayers claiming the Earned Income Tax Credit (EITC). Using counterfactual audit selection models to explore why the disparity arises, we find that maximizing the detection of underreported taxes would not lead to Black EITC claimants being audited at higher rates. Rather, the audit disparity among EITC claimants stems largely from a policy decision to prioritize detecting overclaims of refundable credits over other forms of noncompliance. Modifying the audit selection algorithm to target total underreported taxes while holding fixed the number of audited EITC claimants would reduce the share of audited taxpayers who are Black and would lead to more audits focused on accurate reporting of business income and deductions, fewer audits focused on the eligibility of claimed dependents, higher per audit costs, and more detected noncompliance.

On image transformation for partial discharge source identification in vehicle cable terminals of high‐speed trains

AbstractPartial discharge (PD) detection of cable terminals is crucial for the safe operation of the traction power system in trains. However, similar PD signals in complex train‐operating environments cause difficulty to recognise the insulation defects. Therefore, a PD signal image transformation recognition method is proposed for PD detection of cable terminal defects to identify defects in cable terminals with similar PD characteristics accurately. In the proposed method, the raw PD signals are firstly transformed to images via the Gramian angular field (GAF) representation. This can reveal the discriminative characteristics embedded in the original PD signals and subsequently facilitate differentiating the PD sources, which exhibit similar characteristic in the time domain. The obtained GAF representation of PD signals (named as PD GAF images) is extracted from local and global features to train an efficient MobileVIT model, which is then utilised to identify similar types of PD sources in cable terminals. The results show that the proposed method achieves 97.5% recognition accuracy in the field experiment, which is superior to other methods.

Statistical Identification of Independent Shocks with Kernel-based Maximum Likelihood Estimation and an Application to the Global Crude Oil Market

Independent component analysis has emerged as a promising approach for revealing structural relationships in multivariate dynamic systems, particularly in scenarios with limited knowledge of causal patterns. This article introduces a robust kernel-based maximum likelihood (KML) estimation method that accommodates the distributional characteristics of the structural sources of data variation. Our Monte Carlo study demonstrates the superior performance of the KML estimator compared to existing approaches for independence-based identification. Moreover, the proposed method enables partial identification and dimension reduction even in the presence of dependent shocks. We illustrate the benefits of our approach by applying it to the global oil market model of Kilian, highlighting its ability to capture unmodeled higher-order dependence between oil supply and speculative oil demand shocks.

Inoculation with Jeotgalicoccus sp. improves nutritional quality and biological value of Eruca sativa by enhancing amino acid and phenolic metabolism and increasing mineral uptake, unsaturated fatty acids, vitamins, and antioxidants.

Plant growth-promoting bacteria (PGPB) are considered a promising tool for triggering the synthesis of bioactive compounds in plants and to produce healthy foods. This study aimed to demonstrate the impact of PGPB on the growth, accumulation of primary and secondary metabolites, biological activities, and nutritional qualities of Eruca sativa (arugula), a key leafy vegetable worldwide. To this end, Jeotgalicoccus sp. (JW0823), was isolated and identified by using partial 16S rDNA-based identification and phylogenetic analysis. The findings revealed that JW0823 significantly boosted plant biomass production by about 45% (P<0.05) and enhanced pigment contents by 47.5% to 83.8%. JW0823-treated plants showed remarkable improvements in their proximate composition and vitamin contents, with vitamin E levels increasing by 161.5%. JW0823 induced the accumulation of bioactive metabolites including antioxidants, vitamins, unsaturated fatty acids, and essential amino acids, thereby improving the nutritional qualities of treated plants. An increase in the amounts of amino acids was recorded, with isoleucine showing the highest increase of 270.2%. This was accompanied by increased activity of the key enzymes involved in amino acid biosynthesis, including glutamine synthase, dihydrodipicolinate synthase, cystathionine γ-synthase, and phenylalanine ammonia-lyase enzymes. Consequently, the total antioxidant and antidiabetic activities of the inoculated plants were enhanced. Additionally, JW0823 improved antimicrobial activity against several pathogenic microorganisms. Overall, the JW0823 treatment is a highly promising method for enhancing the health-promoting properties and biological characteristics of E. sativa, making it a valuable tool for improving the quality of this important leafy vegetable.

Rapid and accurate identification of steel alloys by femtosecond laser-ablation spark-induced breakdown spectroscopy and machine learning

This work investigates the application of femtosecond laser-ablation spark-induced breakdown spectroscopy (fs-LA-SIBS) combined with machine learning algorithms for the rapid and accurate identification of steel alloys. Three algorithms, namely random forest (RF), support vector machine (SVM), and partial least squares identification analysis (PLS-DA), were compared and evaluated. The results indicate that, in 100 independent classifications, the RF model demonstrated an average accuracy of 0.9337, significantly surpassing the accuracies of the SVM model at 0.8281 and the PLS-DA model at 0.8646. In addition, in the evaluation of 5-fold cross-validation and the prediction set, the RF model achieved a near-perfect micro-average area under curve (AUC) of 0.9996, surpassing the AUCs of the SVM model at 0.9761 and the PLS-DA model at 0.9847. The PCA results provided valuable insights into the spectral features that most significantly contributed to the classification accuracy, further confirming the RF model's robustness and effectiveness. This integrated approach offers a powerful tool for the rapid classification and accurate identification of steel alloys in industrial applications.

A material flow or life cycle analysis perspective for the Water-Energy-Food nexus assessment of organisations? A comparative study

Nowadays, food production systems play a relevant role as the steady increase of global population and food demand. The water-energy-food (WEF) nexus is a suitable approach to tackle resources management associated with these three pillars recognizing synergies and trade-offs. Different approaches have been used in the literature to measure the WEF nexus, being material flow analysis (MFA) and life cycle assessment (LCA), two of the most proven methodologies. The MFA approach is based on the amount of resources consumed, while using the LCA perspective considers all flows of the system (LCA footprints approach) or considering only the flows associated with water, energy, and food pillars as the inventory data (WEF-LCA approach). This manuscript compares the three mentioned approaches to identify their strengths and weaknesses. To do this, a sample of 100 Spanish dairy farms is analysed, where a single WEF nexus index (WEFni) is obtained using Data Envelopment Analysis. Results show that only four farms achieved a WEFni equal to 100 in all approaches, while the main difference between them is the number and type of resources considered for calculating the WEF nexus, which could imply a partial identification of hotspots of food systems.

Instrument-Based Estimation of Full Treatment Effects with Partial Compliers

Abstract The effect of the full treatment is a primary parameter of interest in policy evaluation, while often the effect of a subset of treatment is estimated. We partially identify the local average treatment effect of receiving full treatment (LAFTE) using an instrumental variable that may induce individuals into subsets of treatment (partial compliers). We show that partial compliers violate the standard exclusion restriction, necessary conditions on the absence of partial compliers are testable, and partial identification holds under a double exclusion restriction. We identify partial compliers in four applications and estimate informative bounds on the LAFTE in three of them.

Transformer Fault Diagnosis Based on Intelligent Algorithm and Partial Discharge

Abstract As an important part of power transmission and transformation equipment and power systems, transformers’ operating conditions will have a direct impact on the stability and reliability of the power system. In view of the problems of high diagnosis cost and low accuracy of diagnosis results in existing fault diagnosis technology, this paper uses the pulse current method to detect the pulse signal generated by the discharge model based on the partial discharge experimental platform data, analyzes and studies the discharge characteristics of different discharge defects, and draws For the partial discharge phase distribution (PRPD) spectrum of each defect type, the statistical characteristic parameters characterizing the characteristics of the spectrum were extracted. The partial discharge statistical characteristics were used as classification feature quantities and input into three network models: RBF, GRNN, and PSO-GRNN. The results show that PSO-GRNN has a good recognition effect and is suitable for partial discharge type identification. It has important practical significance and application value for the all-round health management of power transformers.

Research on discharge characteristics of synthetic ester-paper insulation under power frequency and low frequency

Abstract Synthetic ester insulating oil transformer has broad application prospects in offshore wind power low-frequency power transmission. This paper carried out discharge tests on synthetic ester-paper insulated used sharp plate electrodes at a power frequency of 50 Hz and a low frequency of 17 Hz based on the pulse current method. The research results show that some basic characteristics do not change under 50 Hz and 17 Hz, that is, the discharge is distributed near the voltage peak in a peak shape. The main differences are that the positive peak characteristics at 17 Hz tend to be blurred; the positive maximum discharge amount, and discharge times show an overall downward trend; the phase-resolved partial discharge pattern (PRPD) and the statistical map (n-φ, n-q) have the same overall shape, but different distribution ranges. The test results can be used as a reference for partial discharge pattern identification and insulation design in synthetic ester low-frequency transformers.

Bounding program benefits when participation is misreported: Estimation and inference with Stata

Instrumental-variables estimation is an approach commonly used to evaluate the effect of a program in case of noncompliance. However, when the binary treatment status is misreported, standard techniques are not sufficient to point identify and consistently estimate the effect of interest. We present a new command, ivbounds, that implements three partial identification strategies developed by Tommasi and Zhang (2024, Journal of Econometrics 238: 105556) to bound the heterogeneous treatment effect when both noncompliance and misreporting of treatment status are present. We illustrate the use of the command by reassessing the benefits of participating in the 401(k) pension plan on savings in the United States.

Substrate Flexibilities of Norbelladine Synthase and Noroxomaritidine/Norcraugsodine Reductase for Hydroxylated and/or Methoxylated Aldehydes.

Amaryllidaceae alkaloids are structurally diverse natural products with a wide range biological properties, and based on the partial identification of the biosynthetic enzymes, norbelladine would be a common intermediate in the biosynthetic pathways. Previous studies suggested that norbelladine synthase (NBS) catalyzed the condensation reaction of 3,4-dihydroxybenzaldehyde and tyramine to form norcraugsodine, and subsequently, noroxomaritidine/norcraugsodine reductase (NR) catalyzed the nicotinamide adenine dinucleotide phosphate (NADPH)-dependent reduction of norcraugsodine to generate norbelladine. However, recent studies have highlighted possible alternative Amaryllidaceae alkaloid biosynthetic pathways via the formation of isovanillin and vanillin from the 4-O- and 3-O-methylation reactions of 3,4-dihydroxybenzaldehyde, respectively. Herein, we focused on NpsNBS and NpsNR, which were initially identified from Narcissus pseudonarcissus, and explored their substrate recognition tolerance by performing condensation reactions of tyramine with various benzaldehyde derivatives, to shed light on the Amaryllidaceae alkaloid biosynthetic pathway from the viewpoint of the enzymatic properties. The assays revealed that both NpsNBS and NpsNR lacked the abilities to produce 4'-O- and 3'-O-methylnorbelladine from isovanillin and vanillin with tyramine, respectively. These observations thus suggested that Amaryllidaceae alkaloids are biosynthesized from norbelladine, formed through the condensation/reduction reaction of 3,4-dihydroxybenzaldehyde with tyramine.

Clearing the smog ceiling: The impact of women’s political empowerment on air quality in European regions

This study explores the impact of female political empowerment on air quality outcomes. Women’s stronger environmental concerns and pro-environmental behaviors may lead to better air quality as they progressively break the glass ceiling in politics. To test this hypothesis, we employ a novel data set on regional air pollutant emissions and women’s political empowerment for a sample of 230 European regions of 27 EU countries. We apply instrumental variables and partial identification methods to ensure that our results are not influenced by confounding variables. We find that female political empowerment is consistently associated with improved air quality.

Adaptive pinning control to topology identification of output stochastic complex dynamical network

This paper concerns partial topology identification problem of stochastic delayed complex dynamical network. Compared with many network models, the novel output coupling is introduced to the network model of this paper, which does not require all state variables of the vertex to be observable. By utilizing the graph-theoretic method and adaptive pinning control technology, the identification criteria of multi-weighted stochastic complex dynamical networks are obtained. Ultimately, the Lorenz system and small-world network are chosen to do numerical simulations. The simulation results indicate that the successful identification time (SIT) of stochastic delayed complex dynamical network with output coupling is reduced by about 56% on average by comparing with the SIT of the network without output coupling, which implies the superiority of output coupling.

HIV estimation using population-based surveys with non-response: A partial identification approach.

HIV estimation using data from the demographic and health surveys (DHS) is limited by the presence of non-response and test refusals. Conventional adjustments such as imputation require the data to be missing at random. Methods that use instrumental variables allow the possibility that prevalence is different between the respondents and non-respondents, but their performance depends critically on the validity of the instrument. Using Manski's partial identification approach, we form instrumental variable bounds for HIV prevalence from a pool of candidate instruments. Our method does not require all candidate instruments to be valid. We use a simulation study to evaluate and compare our method against its competitors. We illustrate the proposed method using DHS data from Zambia, Malawi and Kenya. Our simulations show that imputation leads to seriously biased results even under mild violations of non-random missingness. Using worst case identification bounds that do not make assumptions about the non-response mechanism is robust but not informative. By taking the union of instrumental variable bounds balances informativeness of the bounds and robustness to inclusion of some invalid instruments. Non-response and refusals are ubiquitous in population based HIV data such as those collected under the DHS. Partial identification bounds provide a robust solution to HIV prevalence estimation without strong assumptions. Union bounds are significantly more informative than the worst case bounds without sacrificing credibility.

Partial discharge fault identification method for GIS equipment based on improved deep learning

AbstractAiming at the problems of large consumption of computational resources and insufficient data feature extraction in the current partial discharge fault identification process of GIS equipment, a partial discharge fault identification method of GIS equipment based on improved deep learning is proposed. Firstly, the audio information of GIS equipment is filtered by a simple power normalised cepstral coefficient (SPNCC). Secondly, the spatial correlation between audio data streams is obtained by a convolutional neural network, the temporal correlation of audio is obtained and the next time slice data stream is predicted by using bi‐directional long short‐term memory (BiLSTM) network, and the attention mechanism is designed to extract deeper data features. Finally, the partial discharge fault identification model of GIS equipment based on improved SPNCC‐CNN‐BiLSTM‐Multi‐att is established, which improves the accuracy of the partial discharge identification method of GIS equipment. Experiments show that when the number of iterations is 100, the accuracy, recall, and F1 value of the proposed GIS equipment partial discharge fault recognition method on the dataset are 0.876, 0.812, and 0.843, respectively.

Food security dynamics and measurement error

AbstractWe examine intra‐ and intergenerational food security dynamics in the United States using longitudinal data from the Panel Study of Income Dynamics (PSID) while accounting for measurement error. We apply recently developed methods on the partial identification of transition matrices and show that accounting for measurement error is crucial as even modest errors can dwarf the information contained in the data. Nonetheless, we find that much can be learned under fairly weak assumptions; the strongest and most informative assumption being that measurement errors are serially uncorrelated. In particular, although the evidence—both intragenerational and intergenerational—is consistent with significant mobility, we also find food security status to be persistent for at least some households in the tails of the distribution. We further document some heterogeneities in dynamics across households differentiated by race and education. Finally, the impact of measurement error in the context of underlying dynamics is widely applicable to other areas of applied microeconomics generally as well as to food security dynamics in less developed countries specifically.