Counterfactual Outcomes Research Articles

Denoising for balanced representation: A diffusion-informed approach to causal effect estimation

Estimating individual treatment effects with observational data is crucial in causal inference. While it faces two major challenges: the absence of counterfactual outcomes and selection bias due to non-random assignment and intervention of confounding variables. This paper specifically addresses these by quantifying the distribution shift between the treated and control groups from a generative view to achieve balanced representation and then predict causal effects. We propose a novel method called Denoising for a Balanced Representation in Treatment Effect Estimation (DBRT). Motivated by the aim to generate analogous distributions in the Diffusion model, which aligns with our goal of achieving similar distributions in the latent space, our approach considers the divergence between the treated and control groups as’noise’ that needs to be removed incrementally. We then construct a network to fit and then denoise it step by step, ensuring a consistent reduction of discrepancies between the two groups. Additionally, to enhance the representation of input features, we incorporate memory vectors into the attention mechanism to capture prior information and combined correlations between covariates. Furthermore, we utilize the Hilbert–Schmidt Independence Criterion (HSIC) within the loss function to constrain the learned representation, ensuring its relevance to the original data. DBRT has demonstrated superior performance on different datasets compared to previous classic works, highlighting its effectiveness in treatment effect estimation.

Experience of regret is unaffected by concurrent working memory load

Background When facing the consequences of decisions, people often experience evaluative emotions such as regret. The experience of regret is the result of the comparison between the actual outcome of one’s actions and a better counterfactual outcome that could have occurred had one acted differently in the past. Despite increasing interest in counterfactual thinking and regret, little is known about the type of cognitive processes involved with regret –whether it is underpinned by reflective or intuitive processes. The extant literature provides contradictory evidence: theoretical assertions and developmental work point towards reflective processes, requiring access to capacity-limited cognitive resources whereas, some experimental and neuroscientific evidence point towards intuitive processes. Methods In the current study, adults (N = 20) completed a commonly used gambling task known to elicit regret under high or low working memory load. Results Ratings of regret were not affected by concurrent working memory load (Bayes Factors provided strong evidence for the null hypothesis). Conclusions We conclude that the experience of regret in a commonly used gambling task is unaffected by concurrent working memory load. This suggests that, in adults, regret is not always reliant on reflective cognitive processes and does not require access to central cognitive resources.

No evidence of increased forest loss from a mining rush in Madagascar’s eastern rainforests

Artisanal and small-scale mining is an important livelihood activity in many biodiversity hotspots. There is substantial international concern about the negative impact of artisanal and small-scale mining on biodiversity, yet in most places this remains poorly understood. We explore the impacts of a high-profile mining rush: the 2016 sapphire rush at Bemainty, Eastern Madagascar, where tens of thousands of miners descended on a protected forest. Media coverage claimed the rush caused hundreds of hectares of deforestation and threatened lemur populations. Using the synthetic control method to estimate counterfactual outcomes, we find no evidence that mining increased forest loss or degradation. Supported by informal interviews and a lemur survey, we argue that mining at Bemainty had limited impacts on the surrounding forest, relative to other threats. Our results highlight the heterogeneity of environmental impacts from artisanal and small-scale mining and emphasize the need for more robust evaluations to inform context-specific policies.

An agent in a world of possibilities: Introductory overview of the special issue

Possibilities are deeply engrained in psychology’s attempts to understand human behavior. This special issue offers diverse and novel insights into the role of possibilities. Two articles on morality show surprising links to mental illness and to counterfactual outcomes: People think doing immoral things is a sign of mental illness, and morally unwelcome outcomes stimulate retroactive imputation of more alternatives. Three articles address classic questions of determinism and choice: Possibilities are shaped by prior events, but pure determinism is useless for psychological theory, and meanwhile some perspectives in modern physics clash with psychological observations and experience. Imitation promotes prosociality but people are highly selective as to what they imitate.

Sensitivity models and bounds under sequential unmeasured confounding in longitudinal studies

Abstract We consider sensitivity analysis for causal inference in a longitudinal study with time-varying treatments and covariates. It is of interest to assess the worst-case possible values of counterfactual outcome means and average treatment effects under sequential unmeasured confounding. We formulate several multi-period sensitivity models to relax the corresponding versions of the assumption of sequential non-confounding. The primary sensitivity model involves only counterfactual outcomes, whereas the joint and product sensitivity models involve both counterfactual covariates and outcomes. We establish and compare explicit representations for the sharp and conservative bounds at the population level through convex optimization, depending only on the observed data. These results provide for the first time a satisfactory generalization from the marginal sensitivity model in the cross-sectional setting.

Children use proximity and ability to infer distinct kinds of counterfactual closeness.

Counterfactual outcomes (i.e., events that did not happen) vary in their closeness to reality. Whereas some are viewed as distant possibilities, others are seen as close, barely unrealized outcomes. Here, we investigate whether young children distinguish between two kinds of counterfactual closeness: one based on proximity and the other on ability. In two experiments, 4-7-year-olds (total N = 304) saw stories where two agents lost a race against a competitor. One of the losing racers finished just behind the winner (proximity), whereas the other losing racer was much faster than the winner (ability) but lost after tripping on a stone. When asked which racer almost won the race, children across the full age range predominantly picked the racer who finished in second place, close behind the winner. However, when asked which racer easily could have won and when asked which racer should have won, children at older ages picked the fastest racer. Together, these findings show that children's understanding of proximity-based closeness is already present at Age 4, earlier than children were previously thought to grasp counterfactual closeness. Moreover, the findings suggest young children have differentiated concepts of counterfactual closeness and do not conflate the two kinds of closeness. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Imputation of Counterfactual Outcomes when the Errors are Predictable

A crucial input into causal inference is the imputed counterfactual outcome. Imputation error can arise because of sampling uncertainty from estimating the prediction model using the untreated observations, or from out-of-sample information not captured by the model. While the literature has focused on sampling uncertainty, it vanishes with the sample size. Often overlooked is the possibility that the out-of-sample error can be informative about the missing counterfactual outcome if it is mutually or serially correlated. Motivated by the best linear unbiased predictor (BLUP) of Goldberger in a time series setting, we propose an improved predictor of potential outcome when the errors are correlated. The proposed PUP is practical as it is not restricted to linear models, can be used with consistent estimators already developed, and improves mean-squared error for a large class of strong mixing error processes. Ignoring predictability in the errors can distort conditional inference. However, the precise impact will depend on the choice of estimator as well as the realized values of the residuals.

Competitive effects of implicit auction on interconnectors: evidence from Japan

Interconnectors play a crucial role in electric power systems. They contribute to balancing demand and supply in real-time, guaranteeing efficient dispatch in wide geographic regions, and increasing competition by creating large markets. However, interconnector capacity is a scarce resource because vertically integrated utilities were required to have generating capacity enough to supply most customers within their operating region under a regulated monopoly. Hence, identifying the efficient allocation method is essential, particularly after recent electricity market restructuring. This study evaluates the competitive effect of the implicit auction on the interconnector transmission capacity. The implicit auction allocates all the interconnector capacity simultaneously with electric energy in the day-ahead market. This method prevents market participants from strategically withholding the physical interconnector capacity ex ante to exercise market power, as allowed under the first come, first served rule. This study empirically shows how the capacity was withheld from the day-ahead market under the first come, first served rule using detailed reservation data. Next, I show that the implicit auction increases interconnector capacity available at the day-ahead market and trade volume. I use machine-learning methods, such as random forest and deep neural networks, to predict the counterfactual market outcomes without implicit auction. I find that the gain from trade under the implicit auction is more than US$55 million per year in Japan, which is more than 100 times the implementation cost of the implicit auction.

Counterfactual thinking induces different neural patterns of memory modification in anxious individuals

Episodic counterfactual thinking (eCFT) is the process of mentally simulating alternate versions of experiences, which confers new phenomenological properties to the original memory and may be a useful therapeutic target for trait anxiety. However, it remains unclear how the neural representations of a memory change during eCFT. We hypothesized that eCFT-induced memory modification is associated with changes to the neural pattern of a memory primarily within the default mode network, moderated by dispositional anxiety levels. We tested this proposal by examining the representational dynamics of eCFT for 39 participants varying in trait anxiety. During eCFT, lateral parietal regions showed progressively more distinct activity patterns, whereas medial frontal neural activity patterns became more similar to those of the original memory. Neural pattern similarity in many default mode network regions was moderated by trait anxiety, where highly anxious individuals exhibited more generalized representations for upward eCFT (better counterfactual outcomes), but more distinct representations for downward eCFT (worse counterfactual outcomes). Our findings illustrate the efficacy of examining eCFT-based memory modification via neural pattern similarity, as well as the intricate interplay between trait anxiety and eCFT generation.

Memory modeling of counterfactual generation.

We use a computational model of memory search to study how people generate counterfactual outcomes in response to an established target outcome. Hierarchical Bayesian model fitting to data from six experiments reveals that counterfactual outcomes that are perceived as more desirable and more likely to occur are also more likely to come to mind and are generated earlier than other outcomes. Additionally, core memory mechanisms such as semantic clustering and word frequency biases have a strong influence on retrieval dynamics in counterfactual thinking. Finally, we find that the set of counterfactuals that come to mind can be manipulated by modifying the total number of counterfactuals that participants are prompted to generate, and our model can predict these effects. Overall, our findings demonstrate how computational memory search models can be integrated with current theories of counterfactual thinking to provide novel insights into the process of generating counterfactual thoughts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Investigating Causal Genetic Effects on Overall Survival of Glioblastoma Patients using Normalizing Flow and Structural Causal Model.

Glioblastoma (GBM) is the most common and aggressive brain tumor with short overall survival (OS) of about 15 months. Understanding the causal factors affecting the patient survival is crucial for disease prognosis and treatment planning. Although previous efforts on survival prediction using multi-omics data has yielded useful predictive models, the causation of the correlated genetic risk factors has not been addressed. Recent advances in causal deep learning models enable the study of causality from complex dataset. In this paper, we leverage the recently proposed structural causal model (SCM) with normalizing flows parameterized by deep networks to perform the counterfactual query to investigate the causal relationship between gene mutation and OS with the presence of other confounders including sex, age and radiomics features. The query amounts to the question that what the survival days will be if the gene mutation status has been changed, i.e., from mutant to non-mutant and vice versa. The trained causal model will infer the counterfactual outcome given the intervention on specific gene mutation. We apply multivariate Cox-PH model to find the genes associated with survival, and investigate the causal genetic effect by comparing the original and counterfactual survival days in a bi-directional fashion. Particularly, the following two scenarios are considered: (1) intervention on a specific gene with non-mutant status to generate the counterfactual survival days as if the gene is mutant, with which the original survival days of the subjects with that mutant gene will be compared; (2) intervention on the gene with mutant status and perform the comparison with survival days of subjects with that non-mutant gene. Our experimental results show that no causation of two correlated genes (NF1, RB1) was revealed in the cohort (n=181), while their genetic effects on OS in terms of prolonging or shortening are generally in accordance with clinical findings.

Empirical analysis of social impacts of a rural social enterprise: insights for local and regional development

ABSTRACT In developing countries the most marginalised and disadvantaged people are in rural, remote and regional areas and social enterprises in these areas – rural social enterprises (RSEs) – have been identified as key development actors in this context. However, their impacts are rarely rigorously measured. Our study fills this gap by measuring an RSE’s social impact in a developing country. A smallholder farmers’ survey (n = 1021) is utilised in a propensity score-based method which allowed us to generate counterfactual and estimate outcomes between members and non-members of an RSE. This method was complemented by a stakeholder focus group discussion. Predictors of participation and social impacts of the RSE are identified besides an evaluation of its interventions. Results generate implications for social enterprise practitioners, supporters and policymakers interested in applying RSEs as local and regional development actors as well as researchers involved in social impact measurement.

Probabilistic Causal Effect Estimation With Global Neural Network Forecasting Models.

We introduce a novel method to estimate the causal effects of an intervention over multiple treated units by combining the techniques of probabilistic forecasting with global forecasting methods using deep learning (DL) models. Considering the counterfactual and synthetic approach for policy evaluation, we recast the causal effect estimation problem as a counterfactual prediction outcome of the treated units in the absence of the treatment. Nevertheless, in contrast to estimating only the counterfactual time series outcome, our work differs from conventional methods by proposing to estimate the counterfactual time series probability distribution based on the past preintervention set of treated and untreated time series. We rely on time series properties and forecasting methods, with shared parameters, applied to stacked univariate time series for causal identification. This article presents DeepProbCP, a framework for producing accurate quantile probabilistic forecasts for the counterfactual outcome, based on training a global autoregressive recurrent neural network model with conditional quantile functions on a large set of related time series. The output of the proposed method is the counterfactual outcome as the spline-based representation of the counterfactual distribution. We demonstrate how this probabilistic methodology added to the global DL technique to forecast the counterfactual trend and distribution outcomes overcomes many challenges faced by the baseline approaches to the policy evaluation problem. Oftentimes, some target interventions affect only the tails or the variance of the treated units' distribution rather than the mean or median, which is usual for skewed or heavy-tailed distributions. Under this scenario, the classical causal effect models based on counterfactual predictions are not capable of accurately capturing or even seeing policy effects. By means of empirical evaluations of synthetic and real-world datasets, we show that our framework delivers more accurate forecasts than the state-of-the-art models, depicting, in which quantiles, the intervention most affected the treated units, unlike the conventional counterfactual inference methods based on nonprobabilistic approaches.

Doubly robust proximal synthetic controls.

To infer the treatment effect for a single treated unit using panel data, synthetic control (SC) methods construct a linear combination of control units' outcomes that mimics the treated unit's pre-treatment outcome trajectory. This linear combination is subsequently used to impute the counterfactual outcomes of the treated unit had it not been treated in the post-treatment period, and used to estimate the treatment effect. Existing SC methods rely on correctly modeling certain aspects of the counterfactual outcome generating mechanism and may require near-perfect matching of the pre-treatment trajectory. Inspired by proximal causal inference, we obtain two novel nonparametric identifying formulas for the average treatment effect for the treated unit: one is based on weighting, and the other combines models for the counterfactual outcome and the weighting function. We introduce the concept of covariate shift to SCs to obtain these identification results conditional on the treatment assignment. We also develop two treatment effect estimators based on these two formulas and generalized method of moments. One new estimator is doubly robust: it is consistent and asymptotically normal if at least one of the outcome and weighting models is correctly specified. We demonstrate the performance of the methods via simulations and apply them to evaluate the effectiveness of a pneumococcal conjugate vaccine on the risk of all-cause pneumonia in Brazil.

Single proxy control.

Negative control variables are sometimes used in nonexperimental studies to detect the presence of confounding by hidden factors. A negative control outcome (NCO) is an outcome that is influenced by unobserved confounders of the exposure effects on the outcome in view, but is not causally impacted by the exposure. Tchetgen Tchetgen (2013) introduced the Control Outcome Calibration Approach (COCA) as a formal NCO counterfactual method to detect and correct for residual confounding bias. For identification, COCA treats the NCO as an error-prone proxy of the treatment-free counterfactual outcome of interest, and involves regressing the NCO on the treatment-free counterfactual, together with a rank-preserving structural model, which assumes a constant individual-level causal effect. In this work, we establish nonparametric COCA identification for the average causal effect for the treated, without requiring rank-preservation, therefore accommodating unrestricted effect heterogeneity across units. This nonparametric identification result has important practical implications, as it provides single-proxy confounding control, in contrast to recently proposed proximal causal inference, which relies for identification on a pair of confounding proxies. For COCA estimation we propose 3 separate strategies: (i) an extended propensity score approach, (ii) an outcome bridge function approach, and (iii) a doubly-robust approach. Finally, we illustrate the proposed methods in an application evaluating the causal impact of a Zika virus outbreak on birth rate in Brazil.

Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothing

We address the Individualized continuous treatment effect (ICTE) estimation problem where we predict the effect of any continuous valued treatment on an individual using ob- servational data. The main challenge in this estimation task is the potential confounding of treatment assignment with in- dividual’s covariates in the training data, whereas during in- ference ICTE requires prediction on independently sampled treatments. In contrast to prior work that relied on regularizers or unstable GAN training, we advocate the direct approach of augmenting training individuals with independently sam- pled treatments and inferred counterfactual outcomes. We in- fer counterfactual outcomes using a two-pronged strategy: a Gradient Interpolation for close-to-observed treatments, and a Gaussian Process based Kernel Smoothing which allows us to down weigh high variance inferences. We evaluate our method on five benchmarks and show that our method out- performs six state-of-the-art methods on the counterfactual estimation error. We analyze the superior performance of our method by showing that (1) our inferred counterfactual re- sponses are more accurate, and (2) adding them to the train- ing data reduces the distributional distance between the con- founded training distribution and test distribution where treat- ment is independent of covariates. Our proposed method is model-agnostic and we show that it improves ICTE accuracy of several existing models.

Explaining Reinforcement Learning Agents through Counterfactual Action Outcomes

Explainable reinforcement learning (XRL) methods aim to help elucidate agent policies and decision-making processes. The majority of XRL approaches focus on local explanations, seeking to shed light on the reasons an agent acts the way it does at a specific world state. While such explanations are both useful and necessary, they typically do not portray the outcomes of the agent's selected choice of action. In this work, we propose ``COViz'', a new local explanation method that visually compares the outcome of an agent's chosen action to a counterfactual one. In contrast to most local explanations that provide state-limited observations of the agent's motivation, our method depicts alternative trajectories the agent could have taken from the given state and their outcomes. We evaluated the usefulness of COViz in supporting people's understanding of agents' preferences and compare it with reward decomposition, a local explanation method that describes an agent's expected utility for different actions by decomposing it into meaningful reward types. Furthermore, we examine the complementary benefits of integrating both methods. Our results show that such integration significantly improved participants' performance.

Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation

Estimating the individuals' potential response to varying treatment doses is crucial for decision-making in areas such as precision medicine and management science. Most recent studies predict counterfactual outcomes by learning a covariate representation that is independent of the treatment variable. However, such independence constraints neglect much of the covariate information that is useful for counterfactual prediction, especially when the treatment variables are continuous. To tackle the above issue, in this paper, we first theoretically demonstrate the importance of the balancing and prognostic representations for unbiased estimation of the heterogeneous dose-response curves, that is, the learned representations are constrained to satisfy the conditional independence between the covariates and both of the treatment variables and the potential responses. Based on this, we propose a novel Contrastive balancing Representation learning Network using a partial distance measure, called CRNet, for estimating the heterogeneous dose-response curves without losing the continuity of treatments. Extensive experiments are conducted on synthetic and real-world datasets demonstrating that our proposal significantly outperforms previous methods.

Adversarially Balanced Representation for Continuous Treatment Effect Estimation

Individual treatment effect (ITE) estimation requires adjusting for the covariate shift between populations with different treatments, and deep representation learning has shown great promise in learning a balanced representation of covariates. However the existing methods mostly consider the scenario of binary treatments. In this paper, we consider the more practical and challenging scenario in which the treatment is a continuous variable (e.g. dosage of a medication), and we address the two main challenges of this setup. We propose the adversarial counterfactual regression network (ACFR) that adversarially minimizes the representation imbalance in terms of KL divergence, and also maintains the impact of the treatment value on the outcome prediction by leveraging an attention mechanism. Theoretically we demonstrate that ACFR objective function is grounded in an upper bound on counterfactual outcome prediction error. Our experimental evaluation on semi-synthetic datasets demonstrates the empirical superiority of ACFR over a range of state-of-the-art methods.

The augmented synthetic control method in public health and biomedical research.

Estimating treatment (or policy or intervention) effects on a single individual or unit has become increasingly important in health and biomedical sciences. One method to estimate these effects is the synthetic control method, which constructs a synthetic control, a weighted average of control units that best matches the treated unit's pre-treatment outcomes and other relevant covariates. The intervention's impact is then estimated by comparing the post-intervention outcomes of the treated unit and its synthetic control, which serves as a proxy for the counterfactual outcome had the treated unit not experienced the intervention. The augmented synthetic control method, a recent adaptation of the synthetic control method, relaxes some of the synthetic control method's assumptions for broader applicability. While synthetic controls have been used in a variety of fields, their use in public health and biomedical research is more recent, and newer methods such as the augmented synthetic control method are underutilized. This paper briefly describes the synthetic control method and its application, explains the augmented synthetic control method and its differences from the synthetic control method, and estimates the effects of an antimalarial initiative in Mozambique using both the synthetic control method and the augmented synthetic control method to highlight the advantages of using the augmented synthetic control method to analyze the impact of interventions implemented in a single region.