Sustainable agricultural practices have become increasingly important due to growing environmental concerns and the urgent need to mitigate the climate crisis. Digital agriculture, through advanced data analysis frameworks, holds promise for promoting these practices. Pesticides are a common tool in agricultural pest control, which are key in ensuring food security but also significantly contribute to the climate crisis. To combat this, Integrated Pest Management (IPM) stands as a climate-smart alternative. We propose a causal and explainable framework for enhancing digital agriculture, using pest management and its sustainable alternative, IPM, as a key example to highlight the contributions of causality and explainability. Despite its potential, IPM faces low adoption rates due to farmers’ skepticism about its effectiveness. To address this challenge, we introduce an advanced data analysis framework tailored to enhance IPM adoption. Our framework provides (i) robust pest population predictions across diverse environments with invariant and causal learning, (ii) explainable pest presence predictions using transparent models, (iii) actionable advice through counterfactual explanations for in-season IPM interventions, (iv) field-specific treatment effect estimations, and (v) assessments of the effectiveness of our advice using causal inference. By incorporating these features, our study illustrates the potential of causality and explainability concepts to enhance digital agriculture regarding promoting climate-smart and sustainable agricultural practices, focusing on the specific case of pest management. In this case, our framework aims to alleviate skepticism and encourage wider adoption of IPM practices among policymakers, agricultural consultants, and farmers.

In this opinion article, we discuss the application of critical realism as an alternative model to the biopsychosocial model in the understanding of psychiatric disorders. Critical realism presents a stratified view of reality and recognises mental disorders as emergent phenomena; that is, their full explanation cannot be reduced to explanations at any lower level of biological processes alone. It thus underscores the significance of the depth of ontology, the interaction between agency and structure, and the context dependency and complex nature of causality. Critical realism provides the conceptual and epistemological basis for a more subtle understanding of the aetiology of psychiatric conditions, which is polyfactorial and includes biological, psychological and social dimensions. Through the realisation of the conceptual and applicative shortcomings in the biopsychosocial model, critical realism promises to advance the understanding of mental disorders and enable a more holistic approach to the problem of people with mental disorders.

Many influential political science articles use close elections to study how important outcomes vary after a certain type of candidate wins, such as a Democrat or a Republican. This politician characteristic regression discontinuity (PCRD) design offers opportunities for inferential leverage but also the potential for confusion. In this article, we clarify what causal claims the PCRD licenses, offering a rigorous causal analysis that points to three principal lessons. First, PCRDs do nothing to isolate the effect of the politician characteristic of interest as apart from other politician characteristics. Second, selection processes (regarding both “who runs” and “which elections are close”) can generate and exacerbate such confounding, as noted in Marshall (2024). Third and more fortunately, this approach does make it possible to estimate the average effect of electing a leader of type “A” vs. “B” in the context of close elections, treating the units as districts, not leaders. We also suggest a set of tools that can aid in falsifying key assumptions, avoiding unwarranted claims, and surfacing mechanisms of interest. We illustrate these issues and tools through a reanalysis of an influential study about what happens when extremists win primaries (Hall 2015).

Do more restrictive voter identification (ID) laws decrease turnout? I argue that in the 2018 London Local elections this was the case. Bromley was the only London borough to pilot a more restrictive ID scheme. The scheme was assessed by the Electoral Commission and Cabinet Office but lacked a good estimate for the impact on turnout. Applying a synthetic difference-in-difference (DID) methodology, which has several benefits compared to traditional DID methods, to turnout data from 2002 to 2018 I show that turnout was between 4.0 and 5.0% points lower than otherwise would be expected. This indicates more restrictive ID laws can meaningfully limit turnout which has implications for future elections if governments chose to implement a more restrictive regime.

Researchers would often like to leverage data from a collection of sources (e.g., meta-analyses of randomized trials, multi-center trials, pooled analyses of observational cohorts) to estimate causal effects in a target population of interest. However, because different data sources typically represent different underlying populations, traditional meta-analytic methods may not produce causally interpretable estimates that apply to any reasonable target population. In this article, we present the CausalMetaR R package, which implements robust and efficient methods to estimate causal effects in a given internal or external target population using multi-source data. The package includes estimators of average and subgroup treatment effects for the entire target population. To produce efficient and robust estimates of causal effects, the package implements doubly robust and non-parametric efficient estimators and supports using flexible data-adaptive (e.g., machine learning techniques) methods and cross-fitting techniques to estimate the nuisance models (e.g., the treatment model, the outcome model). We briefly review the methods, describe the key features of the package, and demonstrate how to use the package through an example. The package aims to facilitate causal analyses in the context of meta-analysis.

Guerini and Moneta (2017) have developed a sophisticated method of providing empirical evidence in support of the relations of causal dependence that macroeconomists engaging in agent-based modelling believe obtain in the target system of their models. The paper presents three problems that get in the way of successful applications of this method: problems that have to do with the potential chaos of the target system, the non-measurability of variables standing for individual or aggregate expectations, and the failure of macroeconomic aggregates to screen off individual expectations from the microeconomic quantities that constitute the aggregates. The paper also discusses the in-principle solvability of the three problems and uses a prominent agent-based model (the Keynes + Schumpeter model of the macroeconomy) as a running example.

Oral argument is the most public and visible part of the U.S. Supreme Court’s decision-making process. Yet what if some advocates are treated differently before the Court solely because of aspects of their identity? In this work, we leverage a causal inference framework to quantify the effect of an advocate’s gender on interruptions of advocates at both the Court-level and the justice-level. Examining nearly four decades of U.S. Supreme Court oral argument transcript data, we identify a clear and consistent gender effect that dwarfs other influences on justice interruption behavior, with female advocates interrupted more frequently than male advocates.

We argue that more female candidates on the ballot will decrease the gender participation gap at the polls. We test this hypothesis with data from Italian local elections between 2008 and 2020, taking advantage of a 2012 law requiring at least a third of local council candidates to be women in localities with 5000+ inhabitants. Exploiting the exogenous geographic variation and timing in the implementation of the electoral reform, we evaluate the effect of this exogenously driven variation in women's candidacy on the gendered voting gap. We find a significant and substantively strong causal relationship between the share of women on the ballot and the gendered gap, driven by an increase in women's, but not men's, participation at the polls.

Making informed clinical decisions based on individualised outcome predictions is the cornerstone of precision psychiatry. Prediction models currently employed in psychiatry rely on algorithms that map a statistical relationship between clinical features (predictors/risk factors) and subsequent clinical outcomes. They rely on associations that overlook the underlying causal structures within the data, including the presence of latent variables, and the evolution of predictors and outcomes over time. As a result, predictions from sparse associative models from routinely collected data are rarely actionable at an individual level. To be actionable, prediction models should address these shortcomings. We provide a brief overview of a general framework for the rationale for implementing causal and actionable predictions using counterfactual explanations to advance predictive modelling studies, which has translational implications. We have included an extensive glossary of terminology used in this paper and the literature (Supplementary Box 1) and provide a concrete example to demonstrate this conceptually, and a reading list for those interested in this field (Supplementary Box 2).

The remuneration of MPs affects who engages in politics. Even if average returns to office are positive, as found in all other studies, some officeholders’ returns are likely negative. Further, the timing of returns to office is crucial as politicians often enjoy delayed compensation like lucrative pensions. Utilizing administrative data for parliamentary candidates in Denmark from 1994 to 2015, we estimate first-time runners’ earnings and total income returns to office. Based on total income, practically all elected MPs experience economic gains during their first term. Computations of life-cycle returns reveal that the 25 percent highest earning candidates (pre-office) have no long-term economic gain from winning. Generally, considering the distribution and timing of returns to office improves studies of how office-holding is economically rewarded.

We construct a framework for meta-analysis and other multi-level data structures that codifies the sources of heterogeneity between studies or settings in treatment effects and examines their implications for analyses. The key idea is to consider, for each of the treatments under investigation, the subject’s potential outcome in each study or setting were he to receive that treatment. We consider four sources of heterogeneity: (1) response inconsistency, whereby a subject’s response to a given treatment would vary across different studies or settings, (2) the grouping of nonequivalent treatments, where two or more treatments are grouped and treated as a single treatment under the incorrect assumption that a subject’s responses to the different treatments would be identical, (3) nonignorable treatment assignment, and (4) response-related variability in the composition of subjects in different studies or settings. We then examine how these sources affect heterogeneity/homogeneity of conditional and unconditional treatment effects. To illustrate the utility of our approach, we re-analyze individual participant data from 29 randomized placebo-controlled studies on the cardiovascular risk of Vioxx, a Cox-2 selective nonsteroidal anti-inflammatory drug approved by the FDA in 1999 for the management of pain and withdrawn from the market in 2004.

Behavioral science researchers have shown strong interest in disaggregating within-person relations from between-person differences (stable traits) using longitudinal data. In this paper, we propose a method of within-person variability score-based causal inference for estimating joint effects of time-varying continuous treatments by controlling for stable traits of persons. After explaining the assumed data-generating process and providing formal definitions of stable trait factors, within-person variability scores, and joint effects of time-varying treatments at the within-person level, we introduce the proposed method, which consists of a two-step analysis. Within-person variability scores for each person, which are disaggregated from stable traits of that person, are first calculated using weights based on a best linear correlation preserving predictor through structural equation modeling (SEM). Causal parameters are then estimated via a potential outcome approach, either marginal structural models (MSMs) or structural nested mean models (SNMMs), using calculated within-person variability scores. Unlike the approach that relies entirely on SEM, the present method does not assume linearity for observed time-varying confounders at the within-person level. We emphasize the use of SNMMs with G-estimation because of its property of being doubly robust to model misspecifications in how observed time-varying confounders are functionally related to treatments/predictors and outcomes at the within-person level. Through simulation, we show that the proposed method can recover causal parameters well and that causal estimates might be severely biased if one does not properly account for stable traits. An empirical application using data regarding sleep habits and mental health status from the Tokyo Teen Cohort study is also provided.

Covariate-adjusted treatment effects are commonly estimated in non-randomized studies. It has been shown that measurement error in covariates can bias treatment effect estimates when not appropriately accounted for. So far, these delineations primarily assumed a true data generating model that included just one single covariate. It is, however, more plausible that the true model consists of more than one covariate. We evaluate when a further covariate may reduce bias due to measurement error in another covariate and in which cases it is not recommended to include a further covariate. We analytically derive the amount of bias related to the fallible covariate’s reliability and systematically disentangle bias compensation and amplification due to an additional covariate. With a fallible covariate, it is not always beneficial to include an additional covariate for adjustment, as the additional covariate can extensively increase the bias. The mechanisms for an increased bias due to an additional covariate can be complex, even in a simple setting of just two covariates. A high reliability of the fallible covariate or a high correlation between the covariates cannot in general prevent from substantial bias. We show distorting effects of a fallible covariate in an empirical example and discuss adjustment for latent covariates as a possible solution.

In the behavioral and social sciences, quasi-experimental and observational studies are used due to the difficulty achieving a random assignment. However, the estimation of differences between groups in observational studies frequently suffers from bias due to differences in the distributions of covariates. To estimate average treatment effects when the treatment variable is binary, Rosenbaum and Rubin (1983a) proposed adjustment methods for pretreatment variables using the propensity score.

However, these studies were interested only in estimating the average causal effect and/or marginal means. In the behavioral and social sciences, a general estimation method is required to estimate parameters in multiple group structural equation modeling where the differences of covariates are adjusted.

We show that a Horvitz-Thompson-type estimator, propensity score weighted M estimator (PWME) is consistent, even when we use estimated propensity scores, and the asymptotic variance of the PWME is shown to be less than that with true propensity scores.

Furthermore, we show that the asymptotic distribution of the propensity score weighted statistic under a null hypothesis is a weighted sum of independent χ12 variables.

We show the method can compare latent variable means with covariates adjusted using propensity scores, which was not feasible by previous methods. We also apply the proposed method for correlated longitudinal binary responses with informative dropout using data from the Longitudinal Study of Aging (LSOA). The results of a simulation study indicate that the proposed estimation method is more robust than the maximum likelihood (ML) estimation method, in that PWME does not require the knowledge of the relationships among dependent variables and covariates.

A central theme of research on human development and psychopathology is whether a therapeutic intervention or a turning-point event, such as a family break-up, alters the trajectory of the behavior under study. This paper lays out and applies a method for using observational longitudinal data to make more confident causal inferences about the impact of such events on developmental trajectories. The method draws upon two distinct lines of research: work on the use of finite mixture modeling to analyze developmental trajectories and work on propensity scores. The essence of the method is to use the posterior probabilities of trajectory group membership from a finite mixture modeling framework, to create balance on lagged outcomes and other covariates established prior to t for the purpose of inferring the impact of first-time treatment at t on the outcome of interest. The approach is demonstrated with an analysis of the impact of gang membership on violent delinquency based on data from a large longitudinal study conducted in Montreal.

Graph-based causal models are a flexible tool for causal inference from observational data. In this paper, we develop a comprehensive framework to define, identify, and estimate a broad class of causal quantities in linearly parametrized graph-based models. The proposed method extends the literature, which mainly focuses on causal effects on the mean level and the variance of an outcome variable. For example, we show how to compute the probability that an outcome variable realizes within a target range of values given an intervention, a causal quantity we refer to as the probability of treatment success. We link graph-based causal quantities defined via the do-operator to parameters of the model implied distribution of the observed variables using so-called causal effect functions. Based on these causal effect functions, we propose estimators for causal quantities and show that these estimators are consistent and converge at a rate of $N^{-1/2}$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$N^{-1/2}$$\end{document} under standard assumptions. Thus, causal quantities can be estimated based on sample sizes that are typically available in the social and behavioral sciences. In case of maximum likelihood estimation, the estimators are asymptotically efficient. We illustrate the proposed method with an example based on empirical data, placing special emphasis on the difference between the interventional and conditional distribution.