Determining consumer preferences and utility is a foundational challenge in economics. They are central in determining consumer behaviour through the utility-maximising consumer decision-making process. However, preferences and utilities are not observable and may not even be known to the individual making the choice; only the outcome is observed in the form of demand. Without the ability to observe the decision-making mechanism, demand estimation becomes a challenging task and current methods fall short due to lack of scalability or ability to identify causal effects. Estimating these effects is critical when considering changes in policy, such as pricing, the impact of taxes and subsidies, and the effect of a tariff. To address the shortcomings of existing methods, we combine revealed preference theory and inverse reinforcement learning to present a novel algorithm, Preference Extraction and Reward Learning (PEARL) which, to the best of our knowledge, is the only algorithm that can uncover a representation of the utility function that best rationalises observed consumer choice data given a specified functional form. We introduce a flexible utility function, the Input-Concave Neural Network which captures complex relationships across goods, including cross-price elasticities. Results show PEARL outperforms the benchmark on both noise-free and noisy synthetic data.

We explore the capability of transformers to address endogeneity in in-context linear regression. Our main finding is that transformers inherently possess a mechanism to handle endogeneity effectively using instrumental variables (IV). First, we demonstrate that the transformer architecture can emulate a gradient-based bi-level optimization procedure that converges to the widely used two-stage least squares $(\textsf{2SLS})$ solution at an exponential rate. Next, we propose an in-context pretraining scheme and provide theoretical guarantees showing that the global minimizer of the pre-training loss achieves a small excess loss. Our extensive experiments validate these theoretical findings, showing that the trained transformer provides more robust and reliable in-context predictions and coefficient estimates than the $\textsf{2SLS}$ method, in the presence of endogeneity.

Dyadic data are ubiquitous in our society. International trade, travels, population flows, military alliances, partnerships between firms, research collaboration, and many others can be represented as dyadic data, where each dyad represents a pair of countries, firms, or individuals, depending on the context. Dyadic data analysis is particularly prevalent in the literature of international trade, where regression-based analysis, the so-called gravity model, serves as a primary analytical approach in these fields since the pioneering work by Tinbergen (1962) (see also, e.g., Anderson, 1979, 2011; Head and Mayer, 2014 and references therein). For reviews of recent econometric literature on dyadic data analysis in general, see, for example, Graham (2020a,b). Despite the popularity of dyadic data, there are only a few causal inference methods tailored specifically for dyadic data analysis, with some exceptions such as Baier and Bergstrand (2009), Arpino et al. (2017), and Nagengast and Yotov (2023). This may be due to the non-standard and complex endogeneity structure often encountered in typical applications of dyadic data. For example, suppose we are interested in the impacts of free trade agreements (FTA) on trade flows between countries. The treatment variable, FTA, should be considered endogenous because both the decision to enter into FTA and the trade outcome should be influenced by each country's economic factors and the economic and political relationship between the countries involved. Thus, if one tries to resolve the endogeneity issue by using the instrumental variables (IV) method, for instance, then he/she needs to prepare at least three different types of IVs: those accounting for confounding factors at the "origin" country, those at the "destination", and pair-specific factors.

Endogeneity, i.e. the dependence between noise and covariates, is a common phenomenon in real data due to omitted variables, strategic behaviours, measurement errors etc. In contrast, the existing analyses of stochastic online linear regression with unbounded noise and linear bandits depend heavily on exogeneity, i.e. the independence between noise and covariates. Motivated by this gap, we study the over-and just-identified Instrumental Variable (IV) regression for stochastic online learning. IV regression and the Two-Stage Least Squares approach to it are widely deployed in economics and causal inference to identify the underlying model from an endogenous dataset. Thus, we propose to use an online variant of Two-Stage Least Squares approach, namely O2SLS, to tackle endogeneity in stochastic online learning. Our analysis shows that O2SLS achieves $\mathcal{O}\left(d_x d_z \log ^2 T\right)$ identification and $\tilde{\mathcal{O}}\left(\gamma \sqrt{d_x T}\right)$ oracle regret after $T$ interactions, where $d_x$ and $d_z$ are the dimensions of covariates and IVs, and $\gamma$ is the bias due to endogeneity. For $\gamma=0$, i.e. under exogeneity, O2SLS achieves $\mathcal{O}\left(d_x^2 \log ^2 T\right)$ oracle regret, which is of the same order as that of the stochastic online ridge. Then, we leverage O2SLS as an oracle to design OFUL-IV, a stochastic linear bandit algorithm that can tackle endogeneity and achieves $\widetilde{\mathcal{O}}\left(\sqrt{d_x d_z T}\right)$ regret. For different datasets with endogeneity, we experimentally show efficiencies of O2SLS and OFUL-IV in terms of regrets.

Pricing based on individual customer characteristics is widely used to maximize sellers' revenues. This work studies offline personalized pricing under endogeneity using an instrumental variable approach. Standard instrumental variable methods in causal inference/econometrics either focus on a discrete treatment space or require the exclusion restriction of instruments from having a direct effect on the outcome, which limits their applicability in personalized pricing. In this paper, we propose a new policy learning method for Personalized pRicing using Invalid iNsTrumental variables (PRINT) for continuous treatment that allow direct effects on the outcome. Specifically, relying on the structural models of revenue and price, we establish the identifiability condition of an optimal pricing strategy under endogeneity with the help of invalid instrumental variables. Based on this new identification, which leads to solving conditional moment restrictions with generalized residual functions, we construct an adversarial min-max estimator and learn an optimal pricing strategy. Furthermore, we establish an asymptotic regret bound to find an optimal pricing strategy. Finally, we demonstrate the effectiveness of the proposed method via extensive simulation studies as well as a real data application from an US online auto loan company.

The Machine Learning Meets Econometrics (MLECON) workshop will serve as an interface for researchers from machine learning and econometrics to understand challenges and recognize opportunities that arise from the synergy between these two disciplines as well as to exchange new ideas that will help propel the fields. Our one-day workshop will consist of invited talks from world-renowned experts, shorter talks from contributed authors, a Gather.Town poster session, and an interdisciplinary panel discussion. To encourage cross-over discussion among those publishing in different venues, the topic of our panel discussion will be "Machine Learning in Social Systems: Challenges and Opportunities from Program Evaluation". It was designed to highlight the complexity of evaluating social and economic programs as well as shortcomings of current approaches in machine learning and opportunities for methodological innovation. These challenges include more complex environments (markets, equilibrium, temporal considerations) and behavior (heterogeneity, delayed effects, unobserved confounders, strategic response). Our team of organizers and program committees is diverse in terms of gender, race, affiliations, country of origin, disciplinary background, and seniority levels. We aim to convene a broad variety of viewpoints on methodological axes (nonparametrics, machine learning, econometrics) as well as areas of application.

In this paper, we study endogeneity problems in algorithmic decision-making where data and actions are interdependent. When there are endogenous covariates in a contextual multi-armed bandit model, a novel bias (self-fulfilling bias) arises because the endogeneity of the covariates spills over to the actions. We propose a class of algorithms to correct for the bias by incorporating instrumental variables into leading online learning algorithms. These algorithms also attain regret levels that match the best known lower bound for the cases without endogeneity. To establish the theoretical properties, we develop a general technique that untangles the interdependence between data and actions.

We are in the middle of a remarkable rise in the use and capability of artificial intelligence. Much of this growth has been fueled by the success of deep learning architectures: models that map from observables to outputs via multiple layers of latent representations. These deep learning algorithms are effective tools for unstructured prediction, and they can be combined in AI systems to solve complex automated reasoning problems. This paper provides a recipe for combining ML algorithms to solve for causal effects in the presence of instrumental variables - sources of treatment randomization that are conditionally independent from the response. We show that a flexible IV specification resolves into two prediction tasks that can be solved with deep neural nets: a first-stage network for treatment prediction and a second-stage network whose loss function involves integration over the conditional treatment distribution. This Deep IV framework imposes some specific structure on the stochastic gradient descent routine used for training, but it is general enough that we can take advantage of off-the-shelf ML capabilities and avoid extensive algorithm customization. We outline how to obtain out-of-sample causal validation in order to avoid over-fit. We also introduce schemes for both Bayesian and frequentist inference: the former via a novel adaptation of dropout training, and the latter via a data splitting routine. 1 Introduction Supervised machine learning (ML) provides a myriad of effective methods for solving prediction tasks. In these tasks, the learning algorithm is trained and validated to do a good job predicting the outcome for future examples from the same data generating process (DGP).

We postulate that the nature of information items plays a vital role in the observed spread of these items in a social network. We capture this intuition by proposing a model that assigns to every information item two parameters: endogeneity and exogeneity. The endogeneity of the item quantifies its tendency to spread primarily through the connections between nodes; the exogeneity quantifies its tendency to be acquired by the nodes, independently of the underlying network. We also extend this item-based model to take into account the openness of each node to new information. We quantify openness by introducing the receptivity of a node. Given a social network and data related to the ordering of adoption of information items by nodes, we develop a maximum-likelihood framework for estimating endogeneity, exogeneity and receptivity parameters. We apply our methodology to synthetic and real data and demonstrate its efficacy as a data-analytic tool.