Quantifying Symptom Causality in Clinical Decision Making: An Exploration Using CausaLM

arXiv:2503.19394 · cs.LG, cs.AI · Submitted 2025-03-25 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Hi, I'm Tom.

Jane: And I'm Jane, here with Lu, Meng and our in-house large language model, Lalam.

Tom: We're looking at the paper: "Quantifying Symptom Causality in Clinical Decision Making".

Jane: Here's the gist of the paper. The gist The work investigates how key symptoms, specifically “chest pain,” causally influence diagnostic predictions in clinical decision-making using a CausaLM framework to move beyond mere correlation.

Tom: Current machine learning approaches often rely on correlational patterns between symptoms and diseases, which risks misdiagnoses when symptoms are ambiguous or common across multiple conditions "Current machine learning approaches to medical diagnosis often rely on correlational patterns between symptoms and diseases, risking misdiagnoses when symptoms are ambiguous or common across multiple conditions".

Jane: The authors aim to investigate the causal influence of key symptoms on diagnostic predictions by generating counterfactual text representations where target concepts are effectively “forgotten” "In this work, we move beyond correlation to investigate the causal influence of key symptoms—specifically “chest pain”—on diagnostic predictions".

Tom: This is achieved by employing Textual Representation-based Average Treatment Effect (TReATE) to quantify how the presence or absence of a symptom shapes the model’s diagnostic outcomes "By employing Textual Representation-based Average Treatment Effect (TReATE), we quantify how the presence or absence of a symptom shapes the model’s diagnostic outcomes".

Title and authors: Tom: Hey Jane, so we're looking at this paper today, "Quantifying Symptom Causality in Clinical Decision Making: An Exploration Using CausaLM." It’s about moving beyond just seeing if symptoms are related to diseases, and trying to figure out if the symptom actually *causes* that prediction.

Jane: Right? It sounds like they want to find out how much of the diagnostic power a specific symptom, like chest pain, really has compared to other things. The title itself is pretty direct about that causal aspect.

Lu: What’s interesting here is their approach using CausaLM to generate counterfactual text representations where target concepts are essentially forgotten. They aren't just looking at correlation; they are trying to simulate what happens when a symptom isn't there at all.

Meng: From an engineering standpoint, simulating that forgetting process sounds complex. How do you actually get that representation without it completely breaking the rest of the language understanding?

Lalam: I see them use a pre-trained BERT model and add a new task head to check for the presence of the treatment concept, then they introduce a gradient reversal layer by a factor lambda to reverse those gradients for causal effect estimation. That sounds like they're trying to isolate that specific influence.

Tom: Exactly! They call this Textual Representation-based Average Treatment Effect or TReATE, which is their metric for quantifying how the presence or absence of a symptom shapes the model’s diagnostic outcomes. It’s a way to give those symptoms an actual causal weight.

Jane: So, if they are doing this, what exactly are they trying to show that's different from just looking at how often chest pain and cancer appear together in medical text?

Lu: They contrast their findings against correlation-based baselines like CONEXP. CONEXP computes differences in predictions without invoking counterfactual reasoning, which is what they are doing with TReATE. That’s a key distinction they’re highlighting.

Meng: I'm curious about the experimental setup. They used the DDXPlus dataset, which has one hundred thirty-four thousand five hundred thirty examples of symptoms to diagnosis. How big is that test set compared to their training data?

Tom: The test set is one hundred thirty-four thousand five hundred examples, and they trained their BERT-TC model on sixty-five thousand samples with lambda equal to six. They found that the loss for the TReATE classification task went from zero point six two seven down to zero point five three, while the MLM and NSP losses also dropped significantly.

Jane: That drop in loss is promising, but what does that actually mean for a doctor using this tool? How does it translate to clinical reasoning?

Lu: The results show that for some diseases, particularly Bronchitis and Anemia, the presence of chest pain significantly shifts the model’s probability distribution. TReATE offers a direct window into how including or excluding concepts changes the predicted disease distribution.

Meng: So, if a doctor sees a prediction where chest pain is present, they can trust that this symptom really pushes the diagnosis toward those specific diseases, even if other symptoms are also there?

Title and authors: Tom: Precisely. They’re saying that for certain conditions like Bronchitis and Anemia, chest pain isn't just a co-occurrence; it has a measurable causal effect on the model's output. It’s about being more systematic in comparing diagnoses.

Jane: That really moves the conversation from "is this symptom present?" to "what is the causal impact of this symptom on the diagnosis?" So, what are they suggesting we improve?

Lu: The paper suggests that this approach could help improve diagnostic reasoning by quantifying symptom causality using CausaLM and TReATE. It’s about moving beyond mere correlation to get a more robust causal interpretation.

Meng: If I were deploying this, my main concern would be the fidelity of the adversarial training process. If the model isn't truly 'forgetting' a concept, then those causal claims might not be as strong as they seem on paper.

Tom: That’s fair. The authors themselves point out that imperfect "forgetting" or partial suppression of the target concept could affect the strength of causal claims because it hinges on how well the adversarial training works.

Jane: So, what’s the big picture implication for decision support tools? How does this change how we think about using these kinds of language models in medicine?

Lu: This research contributes to the growing interest in causality within natural language processing and encourages the application of causal reasoning to medical decision support systems. It lays groundwork for future explorations into more nuanced interventions.

Meng: I think it’s a good step toward making these tools more trustworthy, but they also flag a limitation: disentangling effects when multiple symptoms cooccur can be complicated. That’s something we need to work on if we want to make this production-ready for real traffic.

Tom: It sounds like the paper gives us a clearer way to assess the decision-making behavior of clinical NLP models by contrasting TReATE against correlation metrics like CONEXP, which is valuable for building better tools.

Jane: So, to wrap up, we’ve seen how CausaLM and TReATE allow us to quantify the causal effect of symptoms like chest pain on diagnostic predictions in clinical decision making. It gives doctors a way to see how the presence or absence of a symptom actually steers the model's prediction.

Lu: The core contribution is using counterfactual text representations generated by CausaLM to achieve this, which opens up exploring alternative diagnostic pathways based on what might be missing from the text.

Meng: For deployment, it means we have a metric that’s more grounded in intervention than just observing patterns in the data. It gives us a causal grounding for our decision support tool.

Lalam: I'm confident this approach is solid because they are explicitly defining the causal effect using TReATE, which is mathematically defined as TReATECj,Cm = ⟨Eg

z(f(ϕ(X))): − Eg

z(f(ϕCj,Cm(X))): ⟩.

Tom: So we’ve seen how this research uses CausaLM to quantify symptom causality in clinical decision making. It moves us away from just correlation toward understanding the actual influence of symptoms on what an AI model predicts.

Title and authors: Jane: And while they acknowledge that disentangling effects when many symptoms are present is hard, they provide a principled way to test if a specific symptom has a genuine causal effect on the diagnostic outcome.

Lu: The work suggests that some diseases, like Bronchitis and Anemia, are strongly influenced by chest pain in this way. It validates model representations against what we intuitively expect from clinical knowledge.

Meng: I think for real-world use, it’s about building a system that can offer a causally grounded decision support tool by contrasting these TReATE results against simpler correlation-based metrics like CONEXP.

Tom: We’ve looked at the mechanics of this paper and seen how they use CausaLM to quantify symptom causality in clinical decision making. It’s about getting a more robust and systematic way to compare diagnoses that goes deeper than simple co-occurrence.

Jane: This research contributes to the growing interest in causality within natural language processing and encourages applying causal reasoning to medical decision support systems where it matters most.

Lu: Ultimately, this work contributes to the growing interest in causality within natural language processing and encourages the application of causal reasoning to medical decision support systems, which is important for how we build these kinds of applications.

Meng: So for us shipping models, it means we have a way to measure if an intervention—like simulating the absence of a symptom—actually changes the outcome in a predictable way. It’s about testing that effect under controlled conditions.

Lalam: And I'm confident this approach is solid because they are explicitly defining the causal effect using TReATE, which is mathematically defined as TReATECj,Cm = ⟨Eg

z(f(ϕ(X))): − Eg

z(f(ϕCj,Cm(X))): ⟩.

Jane: This research provides a principled way to test if a specific symptom has a genuine causal effect on the diagnostic outcome, which is important for building more trustworthy tools.

Tom: So we’ve explored how this paper, "Quantifying Symptom Causality in Clinical Decision Making: An Exploration Using CausaLM," uses CausaLM to move beyond simple correlation to investigate the causal influence of symptoms like chest pain on diagnostic predictions.

Jane: We see that they use TReATE as their main metric to quantify this causal effect, showing how the presence or absence of a symptom shifts the model’s predicted disease distribution in specific cases like Bronchitis and Anemia.

Lu: The implication is that we get a more robust way to assess if symptoms actually have an influence on diagnosis, validating model representations against clinical intuition.

Meng: For deployment, it means we have a metric that's more grounded in intervention than just observing patterns in the data, which helps build a decision support tool with causal grounding.

Lalam: And I’m confident this approach is solid because they are explicitly defining the causal effect using TReATE, which is mathematically defined as TReATECj,Cm = ⟨Eg

z(f(ϕ(X))): − Eg

z(f(ϕCj,Cm(X))): ⟩.

The paper's summary: Tom: So, we just finished looking at how they used CausaLM to look at chest pain and disease predictions. Now we’re going to talk about what the paper actually claims is in that summary and what it means for the bigger picture.

Jane: Right, so basically, they're taking these complex symptoms—like chest pain—and using a method called TReATE to figure out if that symptom actually causes an AI model to pick one disease over another, instead of just being related to it by chance.

Lu: They are trying to move past correlation. They’re not just seeing that chest pain and cancer both appear in the same text; they want to know if removing the chest pain text actually changes the AI’s answer about cancer probability.

Meng: That sounds like a lot of engineering overhead, but if they can prove that causal link, it gives us something tangible for deployment instead of just another black box that spits out probabilities.

Tom: Exactly. The key thing is they use these counterfactual text representations generated by CausaLM to simulate what happens when a specific symptom is absent. It lets them explore alternative diagnostic pathways, which is super useful for understanding the model's reasoning process itself.

Jane: And they found that for certain diseases, like Bronchitis or Anemia, the presence of chest pain actually shifts the model’s probability distribution in a measurable way. That’s their main finding—a direct causal effect quantified by TReATE.

Lu: What makes it really interesting is how they compare it to other methods like CONEXP. CONEXP just looks at differences in predictions without trying to actively intervene or simulate forgetting the concept; TReATE takes that extra step and uses counterfactual reasoning for a deeper look at decision-making behavior.

Meng: I’m still worried about the fidelity of that "forgetting." If the adversarial training isn't perfect, those causal claims might be shaky, especially when multiple symptoms are happening at once. That’s the big caveat they mention.

Tom: Yeah, and that leads into how this changes things for decision support tools. They aren't just giving you a list of correlated features; they’re offering a causally grounded tool that shows you exactly how much influence chest pain has on the final diagnosis score.

Jane: It gives clinicians more confidence because it moves the assessment from "is this symptom there?" to "what is the actual impact of this symptom on what we are predicting?" and it validates the model's internal representations against what doctors already know about disease associations.

Lu: Ultimately, they’re contributing to how we understand causality in natural language processing, showing that we can use counterfactual generation not just for generating text but for actually extracting causal signals from diagnostic predictions.

Meng: It sets a high bar for what's needed when we build these systems—we need metrics like TReATE to prove the intervention actually works, not just look good on a test set.

Tom: So while they’ve proven the concept with some diseases, the next step is figuring out how reliable this causal estimation remains when we move it from a controlled dataset to real clinical data where symptoms overlap and context is messy.

The paper's improvements: Tom: So we’re moving on from just looking at chest pain as a symptom to how they suggest making this whole system better and more useful for actual medicine. Now we’re going to talk about the proposed improvements in that paper.

Jane: Right, so after showing us how TReATE works, what are the authors suggesting we should do next to make this tool even more powerful? I mean, how do we take this from a neat research experiment into something a doctor could actually use on their screen?

Lu: The main suggestion is to focus on controlling for multiple confounding variables. Right now, they look at one symptom at a time, but in real life, many symptoms happen together. They want the method to be able to handle those sets of variables simultaneously.

Meng: That makes sense from a production standpoint because if you have five symptoms present and you only test them individually, the model gets confused by all that noise. Being able to control for sets of confounding variables makes the prediction much more reliable at scale.

Tom: From my side as an engineer, I think integrating structured medical knowledge is a huge step. They suggest grounding these textual analyses in actual clinical guidelines or databases so the causal links aren't just statistical artifacts from the text itself.

Jane: That grounds it in reality, doesn’t it? It moves the whole thing away from being purely a language model exercise and into something that respects what doctors actually know about how diseases present.

Lu: Exactly. And they also suggest refining the representation learning itself—how we encode those concepts—to be more robust when dealing with complex co-occurrences. They’re looking at ways to make sure the "forgetting" mechanism is less fragile.

Meng: If you can make that representation learning more stable, it reduces the number of things that break when we push the model into a high-volume environment where data quality isn't perfect. That addresses my main concern about deployment issues.

Tom: So, to recap, they’re suggesting a focus on controlling for sets of confounding variables and integrating structured clinical knowledge to make the causal interpretation more grounded and robust than what they showed in the initial test.

Jane: It suggests that this isn't just about proving one symptom matters; it's about building a system that can handle the messy reality of patient data where symptoms overlap.

Lu: And if we get better at controlling those sets, we might eventually be able to test causal effects for entire disease clusters, not just single symptoms like chest pain or anemia. That’s the ambition there.

Meng: I still need to see some real traffic results on these improvements before I can sign off on deploying anything that relies on these more complex control mechanisms. It has to scale reliably first.

Tom: We’ll keep an eye on those scalability tests, because if we can prove those improvements hold up under heavy load, then this approach becomes a much more systematic way to compare different diagnostic paths than just correlation metrics like CONEXP.

Conclusion: Tom: So we’ve covered how CausaLM uses TReATE to measure the causal effect of symptoms like chest pain on diagnostic predictions in clinical decision making. Now we’re wrapping up and talking about what this all means for where we go next.

Jane: Basically, the work shows that these models can do more than just find correlations; they can start figuring out cause and effect in medical text using counterfactual reasoning to test those causal claims against real clinical intuition.

Lu: It suggests a new way to interpret model outputs where you aren't just looking at what’s present, but what would happen if something crucial was missing. That opens up exploring alternative diagnostic pathways based on what’s absent.

Meng: I still have to stress that this is promising because it moves us away from correlation-based metrics like CONEXP and gives us a causal grounding for decision support tools, which is exactly what we need when we ship something to production.

Lalam: I'm confident the finding that some diseases are strongly influenced by chest pain really validates model representations against what doctors already know about disease associations. It’s a solid answer from my literature review.

Tom: Exactly. This whole paper, "Quantifying Symptom Causality in Clinical Decision Making: An Exploration Using CausaLM," gives us a more robust and systematic way to compare diagnoses that goes deeper than just seeing if symptoms co-occur.

Jane: It contributes to the growing interest in causality within natural language processing, encouraging us to apply causal reasoning when we build medical decision support systems.

Lu: The authors themselves flag the limitation that disentangling effects becomes really hard when multiple symptoms are happening at once, which is a challenge we’ll have to tackle next.

Meng: That's fair because real patient data is never clean, and I think controlling for sets of confounding variables is going to be the next big engineering hurdle if we want to make this work reliably.

Tom: So while they show some strong results for specific diseases, the next phase involves figuring out how reliable this causal estimation remains when we move it from a controlled dataset to real clinical data where symptoms overlap and context is messy.

Jane: It’s a big step forward because it shows we can use counterfactual text representations to explore alternative diagnostic pathways, which is exactly what helps build more trustworthy AI for medicine.

Lu: We need to see those improvements on controlling for sets of confounding variables before we can fully believe this is ready for complex, real-world clinical environments.

Meng: Yeah, I think the focus needs to be on proving that this causal method actually works under stress and not just in a perfect test set scenario.

Lalam: Based on what I’ve seen in the literature, I’m pretty sure this direction for quantifying symptom causality is the one that matters most right now.

Amir Feder, Nadav Oved, Uri Shalit, Roi Reichart

cs.LG, cs.AI

Submitted: 2025-03-25

Updated: 2026-10-21

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 80/100

The gist: The gist The work investigates how key symptoms, specifically “chest pain,” causally influence diagnostic predictions in clinical decision-making using a CausaLM framework to move beyond mere

Key concepts

CausaLM
A framework using a pre-trained BERT model fine-tuned with an additional 'TC head' to determine if a specific treatment concept (like a symptom) exists in the text. It is used to generate counterfactual text representations by attempting to 'forget' the presence of that concept.
TReATE
Textual Representation-based Average Treatment Effect. This metric is used to quantify the causal effect of a symptom on diagnostic predictions. It measures how much the model's probability distribution shifts when a specific concept (symptom) is included or excluded from the text.
Counterfactual Generation
The process of generating modified text where a target concept, such as a symptom, is effectively 'forgotten' or suppressed. This allows researchers to simulate alternative diagnostic pathways by observing how changing the presence of a symptom alters the model's predictions.
CONEXP
A correlation-based metric used for comparison. CONEXP computes differences in predictions without using counterfactual reasoning, relying only on conditional expectations rather than active intervention or simulating what would happen if a symptom were absent.

Summary

The gist The work investigates how key symptoms, specifically “chest pain,” causally influence diagnostic predictions in clinical decision-making using a CausaLM framework to move beyond mere correlation.

Introduction and Motivation

Current machine learning approaches often rely on correlational patterns between symptoms and diseases, which risks misdiagnoses when symptoms are ambiguous or common across multiple conditions Current machine learning approaches to medical diagnosis often rely on correlational patterns between symptoms and diseases, risking misdiagnoses when symptoms are ambiguous or common across multiple conditions. The authors aim to investigate the causal influence of key symptoms on diagnostic predictions by generating counterfactual text representations where target concepts are effectively “forgotten” In this work, we move beyond correlation to investigate the causal influence of key symptoms—specifically “chest pain”—on diagnostic predictions. This is achieved by employing Textual Representation-based Average Treatment Effect (TReATE) to quantify how the presence or absence of a symptom shapes the model’s diagnostic outcomes By employing Textual Representation-based Average Treatment Effect (TReATE), we quantify how the presence or absence of a symptom shapes the model’s diagnostic outcomes.

Methodology and Causal Modeling

The methodology centers on defining causal model explanation, which involves finding the causal effect of a given concept (treatment) on the model’s predictions We’re trying to find the causal effect of a given concept (called treatment) on the model’s predictions.

  1. Language Representation involves hypothesizing a set of binary variables C representing the existence of predefined concepts, where Cj = 1 means the j-th symptom exists in the text We will denote the set of binary variables C = [Cj ∈ [0, 1]j ∈ [0, 1, 2, …, k], where each variable corresponds to the existence of a predefined concept (in our case each symptom)].

  2. Representation-Based Counterfactual Generation uses the CausaLM framework involving a pre-trained BERT model finetuned with an additional TC head to determine if the treatment concept exists in the text We added a new TC head that had the task of determining if the treatment concept exists in the text or not. A gradient reversal layer is added between this head and the rest of the model to reverse gradients for causal effect estimation by a factor λ We then added a gradient reversal layer between the TC head and the rest of the model to reverse the gradients of the loss for the TC classification task by a factor λ. The final BERT-CF model is trained to maximize this classification loss while minimizing losses for MLM and NSP tasks Our final goal was to maximize the TC classification loss and minimize the loss for the MLM and NSP tasks.

Experimental Setup

The experiments utilized the DDXPlus dataset, which contains 134530 examples of symptoms to diagnosis The test set contains 134530 examples of symptoms to diagnosis and the training set contains 1025603 examples which results in 11.60% of the data being the test. The authors trained their BERT-TC model on 65,000 samples with λ=6, observing that while the loss for the TC classification task decreased from 0.627 to 0.53, losses for MLM and NSP tasks also decreased significantly From this, the loss for the TC classification task went from 0.627 to 0.53. To ensure the output distribution matched the data shape, a sparsemax activation function was used instead of softmax To prevent this and match our output with the shape and distribution of our data, we opted to use the sparsemax function (Martins and Astudillo, 2016).

Results and Interpretation

The causal effect was quantified using TReATE, which is defined as TReATECj,Cm = ⟨Eg[⃗z(f(ϕ(X)))] − Eg[⃗z(f(ϕCj,Cm(X)))]⟩ As defined in Feder et al. (2021), the causal effect for the concept Cj controlling for Cm, on the probability distribution ⃗z is: TReATECj,Cm = ⟨Eg[⃗z(f(ϕ(X)))] − Eg[⃗z(f(ϕCj,Cm(X)))]⟩. The results showed that for some diseases, particularly Bronchitis and Anemia, the presence of chest pain significantly shifts the model’s probability distribution Our results show that for some diseases, particularly Bronchitis and Anemia, the presence of chest pain significantly shifts the model’s probability distribution. TReATE offers a direct window into how inclusion or exclusion of concepts changes the predicted disease distribution The TReATE metric offers a direct window into how the inclusion or exclusion of certain concepts (here, the symptom “chest pain”) changes the model’s predicted disease distribution. In comparison, CONEXP provides a simpler correlation-based perspective by computing differences in predictions without invoking counterfactual reasoning CONEXP relies on conditional expectations rather than active intervention i.e., it does not take into account counterfactual representations and only computes the differences in predictions.

Conclusion

The work presents a novel approach to assessing the causal effect of key symptoms on disease prediction models by leveraging CausaLM and TReATE In this work, we have presented a novel approach to assessing the causal effect of key symptoms on disease prediction models by leveraging the CausaLM framework. The findings suggest that some diseases are strongly influenced by chest pain, validating model representations against clinical intuition while providing a more robust causal interpretation than correlation-based metrics Our results indicate that some diseases—particularly those for which chest pain is a clinically recognized symptom—are strongly influenced by the presence of that concept. This research contributes to the growing interest in causality within natural language processing and encourages the application of causal reasoning to medical decision support systems Ultimately, this work contributes to the growing interest in causality within natural language processing, encourages the application of causal reasoning to medical decision support.

Discussion and Future Work

Limitations include potential issues with the fidelity of adversarial training and the complexity of disentangling effects when multiple symptoms cooccur First, the causal interpretation hinges on the fidelity of the adversarial training process. Imperfect “forgetting” or partial suppression of the target concept may affect the strength of causal claims. Future work could explore controlling for sets of confounding variables and integrating structured medical knowledge to ground these analyses more firmly Future work could address these challenges by exploring multiple treatment concepts simultaneously, controlling for sets of confounding variables, and further refining representation learning. This approach lays the groundwork for future explorations into more nuanced interventions and diverse clinical corpora Beyond the current dataset, testing our approach on diverse clinical corpora would help validate its robustness and generalizability.

References

Seppo Enarvi, Marilisa Amoia, Miguel Del-Agua Teba, Brian Delaney, Frank Diehl, Stefan Hahn, Kristina Harris, Liam McGrath, Yue Pan, Joel Pinto, Luca Rubini, Miguel Ruiz. 2020. Generating medical reports from patient-doctor conversations using sequence-tosequence models. In Proceedings of the First Workshop on Natural Language Processing for Medical Conversations Generating medical reports from patient-doctor conversations using sequence-tosequence models.

Amir Feder, Nadav Oved, Uri Shalit, and Roi Reichart. 2021. CausaLM: Causal Model Explanation Through Counterfactual Language Models. Computational Linguistics CausaLM: Causal Model Explanation Through Counterfactual Language Models.

Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2016. Domain-adversarial training of neural networks. Preprint Domain-adversarial training of neural networks.

Seethalakshmi Gopalakrishnan, Luciana Garbayo, and Wlodek Zadrozny. 2024. Causality extraction from medical text using large language models (llms). Preprint Causality extraction from medical text using large language models (llms).

Yash Goyal, Uri Shalit, and Been Kim. 2019. Explaining classifiers with causal concept effect (cace). CoRR Explaining classifiers with causal concept effect (cace).

Keith Harrigian, Ayah Zirikly, Brant Chee, Alya Ahmad, Anne Links, Somnath Saha, Mary Catherine Beach, and Mark Dredze. 2023.

Improvements for AI systems

  1. Improve diagnostic reasoning by quantifying symptom causality using Textual Representation-based Average Treatment Effect (TReATE). The improved system can provide a more robust and systematic way to compare diagnoses by estimating the causal effect that these symptoms have on a specialist referral, moving beyond mere correlation.

  2. Enhance model interpretability through the use of counterfactual text representations generated by CausaLM. This allows the system to explore alternative diagnostic pathways, and the weights given to various symptoms in the diagnostic process by simulating situations where specific symptoms were not observed or not reported.

  3. Develop a more trustworthy decision support tool for clinical practice. The system can offer a causally-grounded decision support tool by contrasting TReATE results against correlation-based baselines like CONEXP, offering a deeper insight into the decision-making behavior of clinical NLP models.

Abstract

Current machine learning approaches to medical diagnosis often rely on correlational patterns between symptoms and diseases, risking misdiagnoses when symptoms are ambiguous or common across multiple conditions. In this work, we move beyond correlation to investigate the causal influence of key symptoms-specifically "chest pain" on diagnostic predictions. Leveraging the CausaLM framework, we generate counterfactual text representations in which target concepts are effectively "forgotten" enabling a principled estimation of the causal effect of that concept on a model's predicted disease distribution. By employing Textual Representation-based Average Treatment Effect (TReATE), we quantify how the presence or absence of a symptom shapes the model's diagnostic outcomes, and contrast these findings against correlation-based baselines such as CONEXP. Our results offer deeper insight into the decision-making behavior of clinical NLP models and have the potential to inform more trustworthy, interpretable, and causally-grounded decision support tools in medical practice.

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