Effect Estimation Research Articles

The impact of the European Union emissions trading system on carbon dioxide emissions: a matrix completion analysis

Despite the negative externalities on the environment and human health, today’s economies still produce excessive carbon dioxide emissions. As a result, governments are trying to shift production and consumption to more sustainable models that reduce the environmental impact of carbon dioxide emissions. The European Union, in particular, has implemented an innovative policy to reduce carbon dioxide emissions by creating a market for emission rights, the emissions trading system. The objective of this paper is to perform a counterfactual analysis to measure the impact of the emissions trading system on the reduction of carbon dioxide emissions. For this purpose, a recently-developed statistical machine learning method called matrix completion with fixed effects estimation is used and compared to traditional econometric techniques. We apply matrix completion with fixed effects estimation to the prediction of missing counterfactual entries of a carbon dioxide emissions matrix whose elements (indexed row-wise by country and column-wise by year) represent emissions without the emissions trading system for country-year pairs. The results obtained, confirmed by robust diagnostic tests, show a significant effect of the emissions trading system on the reduction of carbon dioxide emissions: the majority of European Union countries included in our analysis reduced their total carbon dioxide emissions (associated with selected industries) by about 15.4% during the emissions trading system treatment period 2005–2020, compared to the total carbon dioxide emissions (associated with the same industries) that would have been achieved in the absence of the emissions trading system policy. Finally, several managerial/practical implications of the study are discussed, together with its possible extensions.

Principal quantile treatment effect estimation using principal scores.

Intercurrent events and estimands play a key role in defining the treatment effects of interest precisely. Sometimes the median or other quantiles of outcomes in a principal stratum according to potential occurrence of intercurrent events are of interest in randomized clinical trials. Naïve analyses such as those based on the observed occurrence of the intercurrent events lead to biased results. Therefore, we propose principal quantile treatment effect estimators that can nonparametrically estimate the distribution of potential outcomes by principal score weighting without relying on the exclusion restriction assumption. Our simulation studies show that the proposed method works in situations where the median or quantiles may be regarded as the preferred population-level summary over the mean. We illustrate our proposed method by using data from a randomized controlled trial conducted on patients with nonerosive reflux disease.

Commentary on Chen et al. (2022): The need for continued methodological research on leveraging information in secondary endpoints for more efficient RCTs

Chen et al. (2022) recently proposed a set of estimating equations that incorporate data from secondary endpoints to improve precision in parameter estimates related to a primary endpoint. We were motivated to translate their methodology to the context of randomized controlled trials to gain precision in treatment effect estimation using data from secondary endpoints. Our results suggest that this estimator cannot gain efficiency in this context because of random treatment assignment, especially when there is a treatment effect on secondary endpoints, and that further methodological work in this area is needed.

Balancing the scales? Evaluating the impact of results-based financing on maternal health outcomes and related inequality of opportunity in Zimbabwe

This study evaluates the impact of results-based financing (RBF) on maternal health outcomes and the inequality of opportunity (IOP) in these outcomes in Zimbabwe. We employ a difference-in-differences approach that leverages the staggered implementation of the programme across 60 districts, exploiting temporal variation in the introduction of RBF and individual-level variation in birth timing. Our analysis uses nationally representative, pooled cross-sectional data from the 2005/2006, 2010/2011, and 2015 Zimbabwe demographic and health surveys. Employing the extended two-way fixed effects (ETWFE) estimator to address biases associated with staggered rollouts, we find significant positive effects of RBF on maternal health outcomes. The programme is associated with an increase in the number of prenatal care visits by 0.185 units (p < 0.01), first-trimester care by 7.7 percentage points (pp) (p < 0.01), facility births by 8.6 pp (p < 0.01), and professional delivery assistance by 3.4 pp (p < 0.01), while reducing C-section rates by 1.3 pp (p < 0.01). Additionally, RBF is associated with reductions in IOP in prenatal care visits, early prenatal care, facility births, and professional delivery assistance by 3.8, 1.3, 8.4, and 4.9 pp (p < 0.01), respectively. These findings underscore the potential of RBF to enhance maternal health outcomes and promote health equity. Integrating equity considerations into health system strengthening initiatives is essential. Policymakers should ensure that health interventions improve access and balance opportunities across various socio-economic and demographic groups. This evidence suggests that RBF schemes can improve access to and equity in healthcare services, particularly in low-income settings such as Zimbabwe.

Don't Let Your Analysis Go to Seed: On the Impact of Random Seed on Machine Learning-based Causal Inference.

Machine learning techniques for causal effect estimation can enhance the reliability of epidemiologic analyses, reducing their dependence on correct model specifications. However, the stochastic nature of many machine learning algorithms implies that the results derived from such approaches may be influenced by the random seed that is set before model fitting. In this work, we highlight the substantial influence of random seeds on a popular approach for machine learning-based causal effect estimation, namely doubly robust estimators. We illustrate that varying seeds can yield divergent scientific interpretations of doubly robust estimates produced from the same dataset. We propose techniques for stabilizing results across random seeds and, through an extensive simulation study, demonstrate that these techniques effectively neutralize seed-related variability without compromising the statistical efficiency of the estimators. Based on these findings, we offer practical guidelines to minimize the influence of random seeds in real-world applications, and we encourage researchers to explore the variability due to random seeds when implementing any method that involves random steps.

Commentary: The Seedy Side of Causal Effect Estimation with Machine Learning.

Commentary: The Seedy Side of Causal Effect Estimation with Machine Learning.

Nonstationary longitudinal autoregressive mixed model for count data with measurement error in covariates: Estimation and asymptotics

Several methods have been proposed in the literature for computing unbiased and efficient estimates of the parameters of generalized linear models when the covariates are measured with error. However, to our knowledge, no documented research on computational techniques for parameter estimation currently exist in the literature when the data is a longitudinal count data influenced by an unobservable latent variable and observable covariates that are measured with error. In this paper, we propose a nonstationary conditionally Poisson mixed model for such data and develop unbiased estimating equations with iterative methods for computing estimates of the effect of the covariates, variance of the latent variable, and the correlation index parameter. The performance of the iterative methods is examined through extensive simulation studies. The results show that the methods performed well when the magnitude of the measurement error is not so large as to dominate or mask the effect of the true covariates. Using observed longitudinal count data on the number of patents awarded to 168 firms in the United States from 1974 to 1979 along with associated covariate information on the type of firm, log of the book value of capital in 1972 and research and development (R &amp; D) expenditures we have demonstrated how the methods proposed in this paper can be applied to a real data. In addition, we derive the influence function of the estimator of the covariate effect and discuss the asymptotic properties of the estimator. Journal of Statistical Research 2024, Vol. 58, No. 1, pp. 151-180.

The Impact of Board Gender Diversity on European Firms’ Performance: The Moderating Role of Liquidity

This study examines how board gender diversity affects listed non-financial European companies’ financial performance. Data from the Refinitiv Eikon Platform—LSEG and World Bank databases was used to complete the analysis. The total sample included 4257 companies for the period 2011–2023. This study examined board gender diversity and its interaction with liquidity while controlling for board characteristics such as board size, independence, and board meetings. Controlling for firm characteristics (firm size and leverage) and macroeconomic variables like inflation and GDP. This study estimated the connection using panel regression. Due to Hausman test significance, fixed effect estimation was used. The findings demonstrated a notable and favorable influence of board features, such as gender diversity, board independence, and board size, on European nonfinancial companies. Additionally, liquidity positively affects firm performance. Furthermore, the findings indicated that leverage had a significant negative impact on profitability. Finally, both the size and GDP have a significant beneficial impact on profitability. Our findings indicate that an increased representation of women on the board of directors is associated with greater independence among board members and a higher number of board members being hired. This, in turn, has a positive impact on profitability due to the extensive experience shared among board members. Additionally, this leads to improved governance, enabling better control over decisions and a greater focus on the long-term investment strategy of the company. Our results are robust, as are similar results reported by the GMM regression.

Difference-in-differences Estimator of Quantile Treatment Effect on the Treated

We propose a new difference-in-differences (DID) estimator of the quantile treatment effect on the treated (QTT). The model assumes a common time effect on the cumulative distribution functions of untreated potential outcomes, allowing for covariates. This condition holds if and only if the net change in the untreated outcome densities is common across treated and control groups. Unlike the Changes-in-Changes model proposed by Athey and Imbens (2006), our model is compatible with the usual DID assumption for means, and it provides a computationally simple and straightforward way to control for covariates. We establish uniform consistency and weak convergence of the proposed estimator of QTT and the related functions. The estimators and the simultaneous confidence bands remain valid even for discrete outcome variables. As an empirical application, the distributional impact of the earned income tax credit on birth weight is investigated. We provide a STATA ado file package.

Can world heritage sites boost sales in non‐tourism firms? The role of visibility

AbstractWorld Heritage sites can positively impact local manufacturing industries. Using two‐way fixed effects estimation, we find that manufacturing firms in cities with World Heritage sites see a 9% increase in operating revenue. This finding is robust to matching methods and a control function method. The impact is especially strong for businesses with low online visibility, highlighting that World Heritage sites enhance visibility toward lesser‐known enterprises. The study also shows that the positive impact on sales is greater in cities with better support for economic development, consistent with the notion that firms need good industrial infrastructure and financial resources to produce high‐quality goods.

Unveiling the causal relationship between circulating levels of micronutrients and risk of facial aging: A Mendelian randomization study.

Facial aging involves a continuous sequence of complex, interrelated events that impact numerous facial tissues. The aim of the study was to elucidate the casual relationship between circulating micronutrients and risk of facial aging. A two-sample Mendelian randomization analysis was performed using genetic data from genome-wide association studies. The inverse-variance weighted method is used for causal effect estimation, and additional tools such as Mendelian randomization-Egger, weighted median, simple mode, and weighted mode were used to refine the analysis. We conducted an in-depth examination of the correlation between several micronutrient blood levels and the risk of facial aging, and identified 3 key micronutrients (selenium, carotene, and iron) that may have a significant impact on skin health. Inverse-variance weighted results indicate that selenium levels were positively correlated with the risk of facial aging (odds ratio [OR] 1.005, P = .027), while a negative causal effect of carotene (OR 0.979, P = .024) and iron (OR 0.976, P = .009) on age-related facial alterations was observed. This study offers a new and insightful perspective on the current understanding of antiaging strategies, particularly the importance of appropriate consumption of essential micronutrients to maintain healthy skin condition.

Causal Effect Estimation on Imaging and Clinical Data for Treatment Decision Support of Aneurysmal Subarachnoid Hemorrhage.

Aneurysmal subarachnoid hemorrhage is a medical emergency of brain that has high mortality and poor prognosis. Causal effect estimation of treatment strategies on patient outcomes is crucial for aneurysmal subarachnoid hemorrhage treatment decision-making. However, most existing studies on treatment decision-making support of this disease are unable to simultaneously compare the potential outcomes of different treatments for a patient. Furthermore, these studies fail to harmoniously integrate the imaging data with non-imaging clinical data, both of which are useful in clinical scenarios. In this paper, we estimate the causal effect of various treatments on patients with aneurysmal subarachnoid hemorrhage by integrating plain CT with non-imaging clinical data, which is represented using structured tabular data. Specifically, we first propose a novel scheme that uses multi-modality confounders distillation architecture to predict the treatment outcome and treatment assignment simultaneously. With these distilled confounder features, we design an imaging and non-imaging interaction representation learning strategy to use the complementary information extracted from different modalities to balance the feature distribution of different treatment groups. We have conducted extensive experiments using a clinical dataset of 656 subarachnoid hemorrhage cases, which was collected from the Hospital Authority Data Collaboration Laboratory in Hong Kong. Our method shows consistent improvements on the evaluation metrics of treatment effect estimation, achieving state-of-the-art results over strong competitors. Code is released at https://github.com/med-air/TOP-aSAH.

Discovering Ancestral Instrumental Variables for Causal Inference From Observational Data.

Instrumental variable (IV) is a powerful approach to inferring the causal effect of a treatment on an outcome of interest from observational data even when there exist latent confounders between the treatment and the outcome. However, existing IV methods require that an IV is selected and justified with domain knowledge. An invalid IV may lead to biased estimates. Hence, discovering a valid IV is critical to the applications of IV methods. In this article, we study and design a data-driven algorithm to discover valid IVs from data under mild assumptions. We develop the theory based on partial ancestral graphs (PAGs) to support the search for a set of candidate ancestral IVs (AIVs), and for each possible AIV, the identification of its conditioning set. Based on the theory, we propose a data-driven algorithm to discover a pair of IVs from data. The experiments on synthetic and real-world datasets show that the developed IV discovery algorithm estimates accurate estimates of causal effects in comparison with the state-of-the-art IV-based causal effect estimators.

The Causal Roadmap and Simulations to Improve the Rigor and Reproducibility of Real-data Applications.

The Causal Roadmap outlines a systematic approach to asking and answering questions of cause and effect: define the quantity of interest, evaluate needed assumptions, conduct statistical estimation, and carefully interpret results. To protect research integrity, it is essential that the algorithm for statistical estimation and inference be prespecified prior to conducting any effectiveness analyses. However, it is often unclear which algorithm will perform optimally for the real-data application. Instead, there is a temptation to simply implement one's favorite algorithm, recycling prior code or relying on the default settings of a computing package. Here, we call for the use of simulations that realistically reflect the application, including key characteristics such as strong confounding and dependent or missing outcomes, to objectively compare candidate estimators and facilitate full specification of the statistical analysis plan. Such simulations are informed by the Causal Roadmap and conducted after data collection but prior to effect estimation. We illustrate with two worked examples. First, in an observational longitudinal study, we use outcome-blind simulations to inform nuisance parameter estimation and variance estimation for longitudinal targeted minimum loss-based estimation. Second, in a cluster randomized trial with missing outcomes, we use treatment-blind simulations to examine type-I error control in two-stage targeted minimum loss-based estimation. In both examples, realistic simulations empower us to prespecify an estimation approach that is expected to have strong finite sample performance and also yield quality-controlled computing code for the actual analysis. Together, this process helps to improve the rigor and reproducibility of our research.

Optimal designs using generalized estimating equations in cluster randomized crossover and stepped wedge trials.

Cluster randomized crossover and stepped wedge cluster randomized trials are two types of longitudinal cluster randomized trials that leverage both the within- and between-cluster comparisons to estimate the treatment effect and are increasingly used in healthcare delivery and implementation science research. While the variance expressions of estimated treatment effect have been previously developed from the method of generalized estimating equations for analyzing cluster randomized crossover trials and stepped wedge cluster randomized trials, little guidance has been provided for optimal designs to ensure maximum efficiency. Here, an optimal design refers to the combination of optimal cluster-period size and optimal number of clusters that provide the smallest variance of the treatment effect estimator or maximum efficiency under a fixed total budget. In this work, we develop optimal designs for multiple-period cluster randomized crossover trials and stepped wedge cluster randomized trials with continuous outcomes, including both closed-cohort and repeated cross-sectional sampling schemes. Local optimal design algorithms are proposed when the correlation parameters in the working correlation structure are known. MaxiMin optimal design algorithms are proposed when the exact values are unavailable, but investigators may specify a range of correlation values. The closed-form formulae of local optimal design and MaxiMin optimal design are derived for multiple-period cluster randomized crossover trials, where the cluster-period size and number of clusters are decimal. The decimal estimates from closed-form formulae can then be used to investigate the performances of integer estimates from local optimal design and MaxiMin optimal design algorithms. One unique contribution from this work, compared to the previous optimal design research, is that we adopt constrained optimization techniques to obtain integer estimates under the MaxiMin optimal design. To assist practical implementation, we also develop four SAS macros to find local optimal designs and MaxiMin optimal designs.

Estimating causal effects for binary outcomes using per-decision inverse probability weighting.

Micro-randomized trials are commonly conducted for optimizing mobile health interventions such as push notifications for behavior change. In analyzing such trials, causal excursion effects are often of primary interest, and their estimation typically involves inverse probability weighting (IPW). However, in a micro-randomized trial, additional treatments can often occur during the time window over which an outcome is defined, and this can greatly inflate the variance of the causal effect estimator because IPW would involve a product of numerous weights. To reduce variance and improve estimation efficiency, we propose two new estimators using a modified version of IPW, which we call "per-decision IPW." The second estimator further improves efficiency using the projection idea from the semiparametric efficiency theory. These estimators are applicable when the outcome is binary and can be expressed as the maximum of a series of sub-outcomes defined over sub-intervals of time. We establish the estimators' consistency and asymptotic normality. Through simulation studies and real data applications, we demonstrate substantial efficiency improvement of the proposed estimator over existing estimators. The new estimators can be used to improve the precision of primary and secondary analyses for micro-randomized trials with binary outcomes.

The Impact of Fintech Development on Commercial Banks' Profitability

Fintech is the creative application of technology in developing and delivering financial products and services. Studying its impact on the banking sector, mainly commercial banks, is interesting. Using panel data of 210 Chinese commercial banks from 2011-2018, fixed effects estimation was used to examine the impact of Fintech development on banks' profitability. The findings indicate that Fintech development is significantly and negatively related to bank performance, i.e., Fintech development significantly weakens bank performance. The results are robust to using different Fintech and bank profitability indicators based on endogeneity analysis. On this basis, the heterogeneity factor of the above relationship is also investigated, and it is found that loose monetary policy weakens the negative effect of Fintech on bank profitability. Moreover, for commercial banks with solid monopoly power, the negative impact of Fintech on their performance is mitigated accordingly.

Promoting sustainable agrifood production under climate change: adaptation, returns, and food security implications

ABSTRACT Climate change is challenging sustainable agrifood production and food security, and encouraging farmers’ climate change adaptation can help promote sustainable agrifood production and ensure food security. This study investigates farmers’ climate change adaptation and its impact on agrifood production. We employ the propensity score matching (PSM) model to address the selection bias issue of climate change adaptation and estimate the survey data collected from 415 rice-producing households in rural China. We also estimate the inverse probability weighted regression adjustment (IPWRA) model for robustness check. The empirical results show that farmers’ decisions on climate change adaptation are influenced by household heads’ age, education level, life satisfaction, temperature perception, and transportation conditions. The treatment effect estimations of the PSM model reveal that climate change adaptation significantly increases land productivity by 41.24–44.29% and labor productivity by 55.06–63.72% in rice production. The IPWRA model estimation largely confirms the robustness of the PSM model estimation. We also find that climate change adaptation significantly increases the net returns of rice production. These findings have significant global implications. By understanding the factors influencing farmers’ decisions to adapt to climate change, policymakers worldwide can design targeted interventions to encourage similar practices in other regions. Promoting farmers’ climate change adaptation to increase farm productivity is crucial for ensuring global food security in the face of ongoing climate challenges.

Mitigating ecological footprint in BRICS countries: unveiling the role of disaggregated clean energy, green technology innovation and political stability

BRICS nations as emerging economies have undergone a substantial structural transformation; however, achieving economic growth while sustaining the ecology is a pressing challenge for these economies. Nonetheless, one of the possible ways to achieve ecological sustainability is to promote clean energy use and green innovation backed by a strengthened political system. Thus, this study assesses the effect of disaggregated clean energy use (hydro and nuclear energy), green technology innovation, and political stability on the ecological footprint in BRICS countries from 1993 to 2022 while controlling for the impacts of natural resources rent, population growth, and GDP per capita. Using dynamic fixed effect and CS-ARDL estimators, the outcomes reveal that hydro and nuclear energy use and political stability induce the ecological footprint in BRICS countries. However, the empirical findings indicate that green technology innovation substantially reduces the ecological footprint. Therefore, from a policy perspective, the study encourages policy coordination among BRICS countries to embrace targeted policies to strengthen regulatory frameworks, promote innovations in technological know-how, and encourage the utilization of clean and sustainable energy to foster environmental sustainability.Graphical

The Legitimizing Role of Outsider Board Members: The Case of the U.S. Nonprofit Health Care Sector

Establishing a Nonprofit Hospital’s (NPH’s) legitimacy is central to gaining the support of external stakeholders. Yet, institutional studies have long argued that smaller and less established organizations face a liabilities of smallness/newness problem where they lack the legitimacy of larger and more established organizations. This study develops an institutional explanation of board governance in which outside board members develop a legitimacy that overcomes these liabilities. A panel of 231 U.S. NPHs was used to examine this institutional explanation where Quantile Fixed Effect (QFE) estimations find that the proportion, tenure, and compensation of outside members offer a legitimacy that increases the performance of smaller/newer NPHs over their larger and more established counterparts. This institutional explanation offers a broadened view of board governance that addresses the legitimacy demands of NPHs.