[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117883-en":3,"doc-seo-117883-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117883,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","FAIRNESS IMPLICATIONS OF HETEROGENEOUS TREATMENT EFFECT ESTIMATION WITH MACHINE LEARNING METHODS IN POLICY-MAKING","Causal machine learning methods that flexibly estimate heterogeneous treatment effects can support government policy by producing individual-level impact estimates. The work argues that mainstream AI fairness techniques for predictive decision models are ill-suited to causal ML pipelines, because policy use is typically indirect: the model informs a human decision-maker rather than directly deciding. It defines fairness for joint decision-making as providing information that enables accurate value judgments on just outcomes, then analyzes how causal ML complexity can hinder this goal. The proposed response emphasizes careful modeling and explicit awareness of decision biases rather than traditional fairness adjustments.","arXiv :2309 .00805v1 [ econ .EM] 2 Sep 2023  \nFAIRNESS IMPLICATIONS OF HETEROGENEOUS TREATMENT EFFECT ESTIMATION WITH MACHINE LEARNING METHODS IN  \nPOLICY-MAKING  \nPatrick Rehill  \nCentre for Social Research and Methods Australian National University Canberra [patrick.rehill@anu.edu.au](patrick.rehill@anu.edu.au)  \nNicholas Biddle  \nCentre for Social Research and Methods Australian National University Canberra [nicholas.biddle@anu.edu.au](nicholas.biddle@anu.edu.au)  \nABSTRACT  \nCausal machine learning methods which flexibly generate heterogeneous treatment effect estimates could be very useful tools for governments trying to make and implement policy. However, as the critical artificial intelligence literature has shown, governments must be very careful of unintended consequences when using machine learning models. One way to try and protect against unintended bad outcomes is with AI Fairness methods which seek to create machine learning models where sensitive variables like race or gender do not influence outcomes. In this paper we argue that standard AI Fairness approaches developed for predictive machine learning are not suitable for all causal machine learning applications because causal machine learning generally (at least so far) uses modelling to inform a human who is the ultimate decision-maker while AI Fairness approaches assume a model that is making decisions directly. We define these scenarios as indirect and direct decision-making respectively and suggest that policy-making is best seen as a joint decision where the causal machine learning model usually only has indirect power. We lay out a definition of fairness for this scenario – a model that provides the information a decision-maker needs to accurately make a value judgement about just policy outcomes – and argue that the complexity of causal machine learning models can make this difficult to achieve. The solution here is not traditional AI Fairness adjustments, but careful modelling and awareness of some of the decision-making biases that these methods might encourage which we describe.  \n1 Introduction  \nMethods to estimate heterogeneous treatment effects (HTEs) with causal machine learning methods promise to give policy-makers access to doubly-robust, non-parametric, individual-level estimates of the impact of a policy (Lechner 2023) . This is an intriguing possibility. Data driven exploration of treatment effects may be useful where the peculiarities of a program make existing theory a poor guide to drivers of heterogeneity but researchers have access to a large amount of data (Levin-Rozalis 2000) . However, there are also potential problems that arise from the complexity of these methods. There is an entire literature of critical AI studies which has documented many cases where governments have misused AI systems (more often out of misunderstanding than a genuine desire to harm some group) leading to poor outcomes (Ireni Saban & Sherman 2022) . While these stories all focus on predictive machine learning (ML) applications (i.e. models focused on predicting outcomes rather than predicting treatment effects (Imbens & Rubin 2015)), one cannot help but worry that those who would use causal ML tools may also stumble into poor outcomesin the absence of a critical AI literature examining these methods specifically. While there are many kinds of poor outcomes – transparency issues, technological failures and privacy concerns to name just a few – this paper will focus on one kind of failure – unfairness in outcomes.  \nIn predictive machine learning applications, it can often be useful to use AI Fairness techniques to help make fairer decisions. These approaches broadly try to fit models that equalise outcomes to some extent across different sensitive characteristics like race or gender (Mehrabi et al. 2019) . They in effect trade-off model fit on (often biased) training data  \nRehill and Biddle  \nfor more equitable outcomes. In general, this involves not just mak","cbCaisxQKcxhyCeY","https://ap.wps.com/l/cbCaisxQKcxhyCeY","pdf",351947,1,13,"English","en",105,"# Introduction\n## Heterogeneous treatment effect estimation in causal ML\n## Limits of predictive AI fairness for causal applications\n## Indirect vs direct decision-making and fairness definition\n## Unfairness driven by complexity and bias","[{\"question\":\"Why are standard AI fairness methods not suitable for causal machine learning used in policy?\",\"answer\":\"Standard approaches assume models directly make decisions, while causal ML in policy typically has indirect power by informing a human decision-maker. This mismatch can make predictive fairness adjustments ineffective or inappropriate in causal pipelines.\"},{\"question\":\"How does the paper define indirect decision-making in causal ML policy contexts?\",\"answer\":\"Indirect decision-making occurs when the causal ML model generates estimates to guide a human who performs the final value judgment and policy choice. The human weighs whether outcomes are just.\"},{\"question\":\"What fairness goal does the paper propose for joint human-model policy decisions?\",\"answer\":\"Fairness is defined as the model providing the information a decision-maker needs to make an accurate value judgment about just policy outcomes. Model complexity can obstruct achieving this goal.\"}]","FAIRNESS IMPLICATIONS OF HETEROGENEOUS TREATMENT EFFECT ESTIMATION WITH MACHINE LEARNING METHODS IN POLICY-MAKING | PDF",1785680135,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"fairness-implications-of-heterogeneous-treatment-effect-estimation-with-machine-learning-methods-in-policy-making","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/fairness-implications-of-heterogeneous-treatment-effect-estimation-with-machine-learning-methods-in-policy-making/117883/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are standard AI fairness methods not suitable for causal machine learning used in policy?","Question",{"text":75,"@type":76},"Standard approaches assume models directly make decisions, while causal ML in policy typically has indirect power by informing a human decision-maker. This mismatch can make predictive fairness adjustments ineffective or inappropriate in causal pipelines.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper define indirect decision-making in causal ML policy contexts?",{"text":80,"@type":76},"Indirect decision-making occurs when the causal ML model generates estimates to guide a human who performs the final value judgment and policy choice. The human weighs whether outcomes are just.",{"name":82,"@type":73,"acceptedAnswer":83},"What fairness goal does the paper propose for joint human-model policy decisions?",{"text":84,"@type":76},"Fairness is defined as the model providing the information a decision-maker needs to make an accurate value judgment about just policy outcomes. 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