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AI 根据所提供的论文材料归类：调查校准的异质主体模型用于结构政策情景分析，与微观结构建模最接近；摘要明确未将模拟解释为因果预测。
玻利维亚犯罪与暴力治理政策组合的路径依赖主体微观模拟AI 解读 ↓
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This study develops a survey-calibrated, path-dependent agent-based microsimulation for evaluating crime and violence reduction policies in Bolivia. A synthetic population is constructed from the 2025 Household Survey and the fourth-quarter 2025 Continuous Employment Survey using official expansion weights and cross-survey donor matching. The model combines heterogeneous agents, place-based risk, criminal adaptation, institutional capacity, community organization, and multiple policy channels. Uncertainty is propagated through adaptive Gaussian-process and tree-ensemble emulators, global sensitivity analysis, tempered ensemble MCMC, and predictive meta-calibration. Simulated portfolios include focused policing, community control, Guardabosques-type incentives, social prevention, environmental interventions, justice reforms, investigation, rehabilitation, and financial-network disruption. Results are interpreted as structural policy scenarios rather than causal forecasts, with explicit attention to displacement, distributional effects, implementation capacity, and multi-objective trade-offs.
内生选择与溢出：政策相关因果效应的贝叶斯推断
This paper develops a new econometric framework to identify and estimate policy-relevant causal effects in contexts with endogenous selection into treatment and spillovers within single large networks or spatial settings. Conventional causal inference methods relying on either unconfoundedness or no-interference assumptions are generally inadequate in these scenarios. We introduce a Spillover Roy model that jointly models endogenous treatment selection and potential outcomes…
大规模需求估计的贝叶斯机器学习方法
This work studies how Bayesian machine learning methods can be used for large-scale demand estimation with many product categories. I compare two model classes, a latent factorization model and a mixed logit model and two Bayesian estimation approaches, Markov Chain Monte Carlo (MCMC) and Variational Inference (VI). The analysis combines a simulation study with an application to supermarket scanner data. The results show that the latent factorization model benefits from…
稀疏市场—产品冲击下的离散选择需求模型估计
When credible instruments are scarce or estimates hinge on instrument choice, an alternative to the leading Berry--Levinsohn--Pakes (BLP) approach to demand estimation with aggregate data can be useful. We propose a Bayesian random-coefficients discrete-choice demand estimator that jointly recovers preference parameters and market--product demand shocks under a sparsity restriction, without requiring instrumental variables. Shrinkage priors select a sparse set of active…
从联合分析数据学习偏好：结构模型与深度学习的混合方法
Conjoint experiments randomize multidimensional profiles, yet political science applications typically report only nonparametric averages that do not recover counterfactual choices or individual tradeoffs. We develop a hybrid structural approach for recovering individual preferences from conjoint data. The estimator combines a flexible machine-learning mean preference function, via a deep netural network in our applications, with respondent-level empirical-Bayes updating in…
内生网络形成下的异质同伴效应
This paper introduces a new econometric framework for modeling social interactions with heterogeneous peer responses, addressing endogenous link formation. Our Selection-corrected Heterogeneous Spatial Autoregressive (SCHSAR) approach jointly models link formation and outcome determination. We incorporate a finite mixture structure to capture heterogeneity in peer effects and account for unobserved individual-specific factors driving both network formation and outcome…
识别应得决策规则以评估与实现公平
We study fairness in decision-making when the data may encode systematic bias. Existing approaches typically impose fairness constraints while predicting the observed decision, which may itself be unfair. We propose a novel framework for characterising and addressing fairness issues by introducing the notion of desert decision, a latent variable representing the decision an individual rightfully deserves based on their actions, efforts, or abilities. This formulation shifts…