From Generating Answers to Building Explanations: Integrating Multi-Round RAG and Causal Modeling for Scientific QA (2025)

Abstract: Application of LLMs for complex causal question answering can be stymied by their opacity and propensity for hallucination. Although recent approaches such as Retrieval Augmented Generation and Chain of Thought prompting have improved reliability, we argue current approaches are insufficient and further fail to satisfy key criteria humans use to select and evaluate causal explanations. Inspired by findings from the social sciences, we present an implemented causal QA approach that combines iterative RAG with guidance from a formal model of causation. Our causal model is backed by the Cogent reasoning engine, allowing users to interactively perform counterfactual analysis and refine their answer. Our approach has been integrated into a deployed Collaborative Research Assistant (Cora) and we present a pilot evaluation in the life sciences domain.

## introduction as large language models (llms) demonstrate impressive performance on a wide variety of challenging tasks, there is intense interest in applying them to causal question-answering in complex domains such as life sciences. examples of real queries asked in drug discovery research include: questions like these, which we refer to as complex causal questions, are defined by several challenging characteristics. first, good answers are causal and predictive, requiring the resolution of causal factors to predict an unseen outcome. this resolution often requires multi-step inference as well as integrating information from multiple sources. additionally, multiple correct answers arise from differing but consistent sets of assumptions. applying llms to problems with these characteristics can be stymied by the opacity of their decision making process and propensity for hallucination @xcite . as such, there has been substantial effort to develop techniques that reduce hallucinations and equip llms with observable inferential steps such as retrieval augmented generation (rag) and chain of thought prompting (cot) @xcite . however, causal question answering remains particularly challenging @xcite . we believe one reason is that prior research often neglects the processes by which humans select and evaluate causal explanations. in this paper, we summarize criteria identified from a lengthy history of research in the social sciences as well as the shortcomings of existing llm approaches @xcite . we then present a novel neuro-symbolic approach that addresses these shortcomings by using an executable causal model to guide iterative rag. the resulting causal graph is backed by the cogent reasoning engine, enabling interactive exploration of counterfactual scenarios. our approach has been deployed for pilot users as a part of an existing life sciences research tool, cora @xcite . we evaluate performance on real queries from these pilot users. ## background what makes an answer good or not depends on the task and context of its question. we begin by briefly summarizing findings from the social sciences that shed light on this topic for causal explanations and discuss where current llm approaches fall short. ## approach these shortcomings influenced our approach to creating a causal qa system. it must answer the question by providing an explanation structured by a coherent causal lens, adjust to user expectations via interactive feedback, and allow contrastive exploration. for life sciences research, it must also justify its answer with relevant citations. these criteria merge aspects best expressed symbolically (e.g structured inference) with others best handled by generative methods (e.g. natural language generation and information extraction). for this reason, we designed a neuro-symbolic architecture in which a verbal explanation is generated from an interactive solution graph, as shown in figure 1 , whose semantics are grounded in a cognitively inspired causal formalism. the graph allows the user to add, remove, and edit each node and edge. each concept and relation in the graph is backed by a formal model defined in the cogent reasoning engine @xcite . thus, as the user manipulates the graph, the effect on the target concepts is recomputed in real time, producing a final labeling which we use to update an evidenced natural language answer. we begin by describing the solution graph and its underlying formal model. we then describe how that model acts as a scaffold for iterative rag to construct the solution graph and nl answer. ## evaluation: life sciences we report the results of an evaluation based on a set of 25 multi-hop causal queries sampled from pilot life sciences researchers using cora in production. we compare the natural language answer generated by our approach to those from three commercially available services: gpt4-turbo foot_0 (state of the art llm) , perplexity foot_1 (commercial rag using websearch), elicit foot_2 (commercial rag using semantic scholar), and our solution. this dataset was curated to include answers that required multi-hop inference. in order to avoid confounds due to surface form variations and to facilitate the evaluation, the queries of our dataset were uniformly reformatted using the construction "how does x impact y?". ## conclusions and future work in this paper we have presented a novel approach to complex causal qa, implemented and evaluated as part of a life sciences collaborative research assistant (cora). we argue existing llm approaches are insufficiently performant and fail to address key criteria by which humans generate and evaluate explanations. drawing from the social sciences, we query: how does epigenetic dysregulation of neurotrophins impact ad risk? our system's answer c1 epigenetic dysregulation of neurotrophins, particularly through increased promoter cpg methylation, leads to a down-regulation of brain-derived neurotrophic factor (bdnf) expression, which is associated with decreased synthesis of bdnf in neurons [1][2]. c2 this dysregulation is linked to reduced neuroprotection and has been implicated in the emergence of various neurological and psychiatric diseases [3][4]. c3 bdnf is crucial for cognitive function, promoting neurogenesis, neuronal survival, axonal and dendritic growth, synaptic plasticity, and maintenance in the central nervous system [5]. c4 consequently, decreased levels of bdnf result in the reduction of synaptic proteins, leading to synaptic connection loss, impaired neuronal survival, and adaptability, which are critical factors in neurodegeneration [6]. c5 this pathway of dysregulation contributes significantly to alzheimer's disease (ad) neuropathology, particularly through the increase of hippocampal neurofibrillary tangles (nfts) and plaques (nps), which are strongly correlated with cognitive decline and are hallmark features of ad [7][8]. c6 thus, epigenetic dysregulation of neurotrophins, by decreasing bdnf expression and signaling, directly impacts ad risk by promoting mechanisms that lead to neuronal and synaptic degeneration. elicit's answer e1 epigenetic dysregulation, particularly in the form of dna hypermethylation and histone deacetylation, has been implicated in the pathophysiology of alzheimer's disease (ad) (chouliaras 2010, sanchez-mut 2015, millan 2014). e2 this dysregulation can lead to a repressed chromatin state and reduced plasticity, potentially contributing to the onset and progression of ad (sanchez-mut 2015). e3 the dysregulation of neurotrophins, a family of proteins that support the survival and growth of neurons, is a key aspect of this epigenetic dysregulation (zusso 2018). in particular, the dysregulation of brainderived neurotrophic factor (bdnf) has been linked to ad risk (nativio 2018). e4 this suggests that targeting the epigenetic dysregulation of neurotrophins, including bdnf, could be a potential therapeutic strategy for ad @xcite @xcite . in future work, we plan to expand causal frameworks to include alternatives to qpt. @xcite , for example, argue that life science research also uses a "pathway" model of causation that differs from a mechanistic view. we would like to allow users to design and align their own causal formalism to the solution graph. we also plan to extend our approach to include refuting evidence to counteract confirmation bias and identify competing causal theories.