The ability to perform causal reasoning is widely considered a core feature of intelligence. In this work, we investigate whether large language models (LLMs) can coherently reason about causality. Much of the existing work in natural language processing (NLP) focuses on evaluating commonsense causal reasoning in LLMs, thus failing to assess whether a model can perform causal inference in accordance with a set of well-defined formal rules. To address this, we propose a new NLP task, causal inference in natural language, inspired by the "causal inference engine" postulated by Judea Pearl et al. We compose a large dataset, CLadder, with 10K samples: based on a collection of causal graphs and queries (associational, interventional, and counterfactual), we obtain symbolic questions and ground-truth answers, through an oracle causal inference engine. These are then translated into natural language. We evaluate multiple LLMs on our dataset, and we introduce and evaluate a bespoke chain-of-thought prompting strategy, CausalCoT. We show that our task is highly challenging for LLMs, and we conduct an in-depth analysis to gain deeper insights into the causal reasoning abilities of LLMs. Our data is open-sourced at https://huggingface.co/datasets/causalNLP/cladder, and our code can be found at https://github.com/causalNLP/cladder.
Paper
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Summary
The paper collects a new dataset, named Cladder, that aims at evaluating the causal reasoning abilities of language models. The datasets contain 10,000 questions asking about boolean questions that require intensive causal reasoning. The dataset is constructed based on the idea of a causal inference engine, which covers three rungs of causal inference. Specifically, the authors first sample formal representations of causal inference problems by sampling causal graphs as well as query types. Next, the formal representations are verbalized into natural language problems, where the variables in the formal representations are named with concepts in the stories collected from commonly cited causal inference books. Finally, the representations are verbalized into natural language problems using fixed templates. Using the collected datasets, the paper evaluates a line of language models’ causal reasoning abilities. Experiments results suggest that these problems are still hard for language models in the zero-shot setting (if I understand correctly). In addition, the paper proposes CausalCoT which lets LMs explicitly state causal problems and then solve the problem. The results suggest CausalCoT is better than the baseline.
Strengths
The paper works on an interesting problem, causal inference, and collects a useful dataset consisting of 1,0000 examples. The data creation process is well-designed. The dataset covers multiple rungs and multiple causal graphs. In addition, the alignment also includes anti-commonsense and nonsensical settings, which is essential for controlling possible effects of memorization in LLMs. The paper includes additional error analysis highlighting the LM’s capabilities in performing different steps in the process of solving causal inference problems. The paper is well-written and easy to follow.
Weaknesses
1: The tested baselines (in Table 2) need clarification. Also, the baselines might be somewhat weak. Are the methods tested in Table 2 implemented in zero-shot setting? (I am guessing that based on section 5.6). I wouldn’t be surprised if these language models utterly fail in a zero-shot setting. While the paper tries to frame that providing in-context examples is orthogonal to the paper (section 5.6). I believe it is necessary to benchmark what much better systems can do (e.g., few-shot CoT prompting or few-shot CausalCoT prompting) to understand the capabilities of LMs as well as the potential headroom. 2: The language is synthetic. As suggested in 3.2, the verbalization of the formal causal problem is rule-based, which could result in synthetic and formulaic language. The paper does not provide much evaluation of the language side of the datasets in the main body. 3: No human performance The paper does not provide human performance for this dataset. Although many problems are adapted from books, the templated-based verbalization as well as the nonsensical setting could make some problems tricky even for humans. Thus, human evaluation is still valuable. Also, it can help verify the quality of the datasets.
Questions
Are the baselines all in zero-shot setting? In addition, it would be helpful if the authors can include some full prompt examples in the appendix.
Rating
6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
Confidence
4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness
2 fair
Presentation
3 good
Contribution
3 good
Limitations
The paper discusses the limitations in Section 7.
Summary
The paper introduces a large dataset, CLADDER, designed to evaluate the ability of LLMs to perform causal inference. The dataset includes 10K binary questions about variable association and treatment efficacy along with a ground truth explanation. Questions are answered in the context of an hypothetical closed world in which the available measurements allow to unambiguously answer the questions. Most of the classical causality related NLP tasks focus on commonsense reasoning and domain knowledge, assessing the ability of the model to make deductions based on knowledge of properties of an object or human behavior in everyday life situations. The CLADDER data goes beyond domain knowledge, requiring the model to perform causal inference: inferring the causal graph, describing the treatment effect of interest related to the question (the estimand), choosing an estimator and expressing the numerical results. To build the dataset, a causal graph derived from a list of 10 classical structures is sampled. A query type, describing the treatment effect of interest is then derived. The underlying estimand is then derived and an estimate is obtained using the rules of do-calculus and the abduction-action-prediction steps of counterfactual prediction. All those components, causal graph, query and results are then translated into natural language to be ingested by classical models. For each causal graph a set of 2 to 5 stories taken from the literature are used to ensure that the variable names and their relation are not nonsensical. A template is also used to generate step-by-step explanations. Eight models are then evaluated on the task, with an overall accuracy of 66.64 being reached for GPT-4 and CausalCot, a chain-of-thought prompting strategy that relies on 4 sub questions, corresponding to natural reasoning steps, to guide the model. A nonsensical evaluation, accounting for potential data contamination is also performed. The evaluation is further refined for the CausalCot model, by including an ablation study and assessing the effect of in context learning.
Strengths
Classical causality related NLP tasks mostly focused on commonsense reasoning and domain knowledge. The paper goes beyond that, introducing a new original task, CLADDER, which theoretically requires the model to infer the causal graph, describe the treatment effect of interest, choose an estimator and provide an estimate. The paper nicely builds on the framework proposed by Pearl with a dataset of 10K questions covering a large variety of causal graph structures and estimands. Additionally, the explanation generated together with the questions allows further assessment of the reasoning ability of the model. The evaluation of state of the art models on the task is well conducted with a particular methodology to isolate the effect of data contamination. Additionally, a methodology to boost model's performance using chain-of-thought prompting is proposed. If the improvement is marginal, it allows further investigation on the ability of the model to perform the natural reasoning steps to complete the task.
Weaknesses
To better understand how the information provided is used, it could be interesting to mention a new attribute in the text, completely unrelated for which you provide the marginal probability. Given the low performance, adding noise to the available information is not the priority but it could be nice for future work. On top of the ablation study, it would be interesting to know how the CausalCoT model performs if it was given the question for step 1 along with the answer and then only the question for step 2 to 4. Then doing the same but providing the answer for step 1 and 2. And finally the same for step 1, 2 and 3. It would provide insights on the ability of the model to perform the task if it received partial answers.
Questions
Line 156 _"processes we define specify all necessary information"_ A bit heavy with two verbs following one after the other. I suggest rephrasing, maybe as: "the causal processes defined encapsulates all necessary..." In Figure 4, _Translate the question to a formal estimand_, please note that the notation used with a _\sum_ can be ambiguous as this is only valid for an infinite amount of data (ambiguous outside of the Pearl's community). The estimand doesn't depend on the data observed, the estimator does.
Rating
7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
Confidence
4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness
3 good
Presentation
4 excellent
Contribution
3 good
Limitations
The conclusions made are only valid in a self contained, hypothetical world without any unmentioned factor or causal relationship. A potential negative impact would be for the model to wrongly infer causal effect in the presence of many unmeasured confounders where conditional and marginal are available for few variables. If widely deployed, that kind of model should be able to state the hypothesis it is relying on or state that not enough information is available to draw conclusions on efficacy.
Summary
The author proposes a math-word-style formal causal reasoning dataset, CLADDER, which is the first one that can be used for formally evaluating the causal reasoning/inference ability of LLMs. The authors also proposed a new prompting strategy, CausalCoT, which improves the zero-shot performance of GPT-4 by 2.4% on the proposed dataset.
Strengths
1. The paper is well-written and easy to follow. Illustrations are helpful. 2. It is the first formal causal inference/reasoning dataset in a natural language, math-word-problem-like format, which provide a nice addition to the current logical/math reasoning datasets. 3. It provides a way to formally evaluate the causal reasoning ability of LLMs for the first time.
Weaknesses
1. The causal inference questions used in the dataset seem to be a bit simple for evaluating GP3/GPT4 scale LLMs. To my understanding, they only require a one-step application of a causal inference equation to binary variables. It would be great if hard questions with multi-step reasoning could be included. 2. To my understanding, only zero-shot prompting is used to evaluate the performance of LLMs in Table 2, while the common evaluation paradigm for the reasoning abilities of LLMs is through few-shot CoT prompting. It would be helpful if the authors provided another set of results with few-shot prompting. 3. It would be helpful to actually fine-tune a small LLM (e.g., 7B LLaMa) on the proposed dataset since the dataset is large enough to be trained on (10K). In this case, we can have a better understanding of the difficulty of the proposed dataset, and whether there are spurious correlations/shortcut features in the dataset that can be captured by a statistical model. 4. The authors only apply the proposed CausalCoT method to GPT4, and the improvement seems to be small (2.4%). The evaluation of the proposed prompting technique seems to be insufficient. I'm wondering what is the results when CausalCoT is applied to other models and when it is applied with few-shot in-context learning.
Questions
Please see weaknesses.
Rating
5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
Confidence
3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness
2 fair
Presentation
3 good
Contribution
3 good
Limitations
Yes
Summary
The research question is whether large language models are capable of doing formal causal reasoning as formalized by the approach with SCMs popularized by Pearl. The authors provide a benchmark of 10k examples to evaluate such abilities; they describes in detail the design choices for this benchmark. They then propose a chain-of-thought prompting strategy (CausalCoT) to enhance LLMs with causal reasoning abilities; evaluations are carried out both on OpenAI's proprietary LLMs and open-source LLMs.
Strengths
* The design principles behind the benchmark are well-explained. In particular since LLMs tend to struggle with arithmetic-heavy tasks authors took care of using binary variables and simple causal graphs. * A variety of topologically distinct treatment-effect pairs is considered, covering thus multiple causal graphs. * Non-sensical and anti-commonsense variants of stories are injected to reduce the effect of LLMs having memorized commonsense causal knowledge. Results are reported for the different splits (commonsense, anti-commonsense, etc...). * Good quality figures and diagrams; sufficient background material in the appendix for those not familiar with Pearl's causal framework. * A new application of CoT to formal causal inferences.
Weaknesses
* I have some concerns about whether the research question is appropriate for the conference audience. For example, the cited (in the paper) concurrent work of (Kiciman et al 2023) considers model evaluation on several ways of formalizing causal discovery; this broadens the scope of the potentially interested audience. * In my opinion it would be very important to discuss why this specific approach to evaluating causality matters and the ML community should care. For example, one might argue that human beings have deeply advanced their causal understanding of the world for centuries without using Pearl's framework to draw causal conclusions, so why should we care about LLMs using this specific framework? To draw another analogy, mathematics keeps progressing, yet almost no professional mathematician can write a formal proof or uses a formal proof to discover new math; the interest in understanding if LLMs can write formal proofs or can assist in writing formal proofs should be well-motivated. * If formal causal reasoning abilities matter for discovering new science (e.g.~the quote opening the Introduction), then it would be good to discuss why a setting in which the model uses software tools for causal inference was not considered. Do such tools exist? For example, for formal mathematical proofs this is the case and a natural approach would be to make LLMs make use of such tools. * CausalCoT makes Rung 3 worse (Table 2). IIUC Rung 3 is the most challenging task (counterfactuality), so it would be good to explain why CausalCoT makes the accuracy worse.
Questions
* L. 75; is it correct that Rung i+1 is more difficult than Rung i? Maybe it could be emphasized also in this section. * L. 264: please add an explanation of why temperature=0 * Table 2: Can you explain why CausalCoT makes performance drop on Rung 3, which should be the most challenging one in the Ladder? * I am having trouble to understand how CausalCoT works in practice. Could you point me to where I can find an interaction with the model in the supplementary materials? It would be great if one such example could make it to the paper or the appendix. I am sorry if I missed it somewhere. More specifically, according to this [post](https://www.lesswrong.com/posts/yZb5eFvDoaqB337X5/investigating-causal-understanding-in-llms) some form of few-shot examples are needed to successfully prompt models, so I am wondering if one needs to do the same to get the causal graph representation when using CausalCoT. Overall, my main concern is a lack of discussion or argument for why evaluation on this specific formalization of causal reasoning should matter. I am happy to increase the score if my concerns get addressed.
Rating
5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
Confidence
3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness
3 good
Presentation
3 good
Contribution
2 fair
Limitations
Good. During the rebuttal period the authors agreed to discuss a couple of extra limitations of the work.
Summary
This paper evaluates the causal reasoning ability of language models and proposes a structured prompting approach to improve this ability. For this goal, the authors create a benchmark. The benchmark includes synthesized causal graphs and related queries. The answers to queries are computed based on a causal reasoning formalism given the causal graph. The graph and queries are verbalized for creating the corpus to provide natural language context and questions. For evaluation purposes, a variety of current large language models such as GPT 3.5 and GPT 4 are used. The language models are given the questions that need causal reasoning to be answered. In addition to the new benchmark and the LLM evaluations, the authors propose a structured chain of thought prompting strategy (called CausalCOT) to improve the ability of LLMs in causal reasoning. The idea is to provide step-by-step prompts based on the steps of formal reasoning. The steps include prompting for extraction of the causal graph, classifying the query type, and several others. The results show that structured prompting improves the causal reasoning ability of the large language models. Especially for the questions that are anti-commonsensical and are not contained training large language models, the models show a more considerable extent of improvements compared to the baselines.
Strengths
The paper focuses on an important problem which is causal reasoning with LLMs. The created benchmark is potentially a valuable evaluation resource. Structured prompting is a recent research trend for improving LLMs, and the authors follow the same trend but with a novel structure that is based on causal reasoning steps. The findings show the effectiveness of structured prompting that is in line with other related research.
Weaknesses
--It is not clear to me how the LLMs follow each step of reasoning: Is this totally zero-shot? or do you do in-context learning with a few examples? if this is zero-shot, it is a bit hard to see why the model knows about causal graphs and specific query types for example. --What is the lexicon used for verbalization of the causal model? How many distinct works are used? How do you evaluate if the text that is generated based on a template, is in line with commonsense or not? --The data set is synthetic which is fine. However, do you have any realistic scenarios to test the model on? I mean some realistic problem setting that shows this causal reasoning is required and the structured prompting is helpful in that realistic setting. More specifically, do we have real natural language explanations that explain complex casual graphs? can you add more discussions in this direction to the paper?
Questions
See the above weakness.
Rating
6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
Confidence
4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness
3 good
Presentation
3 good
Contribution
3 good
Limitations
there is a section on this in the paper.
Thanks for your clarification. I appreciate the added experiments and human evaluation, which helps address my concern. I am happy to raise my score.
Thank you for reading our rebuttal materials. We are glad that the newly added experiments and human evaluations help clarify your concerns. And we appreciate your raise of score and positive support. Have a good weekend :)!
Thanks a lot for adding this analysis to the ablation study. It's reassuring to observe a monotonically increasing trend when adding the different steps. I really appreciate your answers and the quality of the paper. I recommend it for acceptance.
Thank you very much for taking time to go through our rebuttal materials. We are glad that the ablation study result provide more insights into CausalCoT, and thank you for suggesting it at the first place. Also, we appreciate your endorsement for the paper, which supports us to continue exploring on this line of research.
I have read the reviews and the author's response. I was already positive about this work but I also see the limitations in verifying the validity of the generated model and the extension to the realistic domain. My score stays unchanged.
Further Questions
Thanks for the explanations and the example prompts. I have some more questions and comments. `Comparison to Kiciman et al.` I am aware of the venue's policy on comparison to concurrent work. While reading the paper under review, the question of why studying this particular formalization of causality is important has come to my mind multiple times. In the related work the authors cite Kiciman et al. and I had a look at that work to get a better understanding of the current debate around causality and LLMs. I found the way in Kiciman et al. the research topic is *presented* quite compelling. In the paper under review I feel the part from L30-50 would need expansion to make the research question look compelling. For example, understanding the difference between correlation and causation (L36-37) or proposing plausible novel hypotheses (L38-39) does not really need Pearl's framework. However, one might point out some examples of the necessity or new deep insights / discoveries brought by this causality framework and improve the motivation for the research question. In summary, I do think a bit more work should be put on presenting and motivating the research question. `Table 2: Can you explain why CausalCoT makes performance drop on Rung 3` I am still confused. I understand that there is a drop in Step 2. However, in Table 3 it seems that adding CasualCoT results in about a 5 points drop from GPT-4 on Rung3, which I find surprising as adding the formal casual reasoning with CoT should help on the most challenging Rung 3. Also on these tasks, what magnitude of accuracy difference is statistically significant? `However, we argue that one important step towards augmenting LLMs is understanding to what extent they already solve certain reasoning tasks.` I am worried that if models use the right tools, they might solve these causality tasks easily. For analogy, an LLM can make simple arithmetic mistakes but if it uses a calculator tool in the right way it can get many arithmetic questions right. While I do not think there is a need to add experimental results with tools, I do think this should be pointed out as a potential limitation of this study and its conclusions on the abilities of causal understanding of LLMs.
`Comparison to Kiciman et al.` We appreciate your interest in our work. And we appreciate the effort you've taken to delve into the Kiciman et al. work to gain a deeper understanding of our research. However, we believe that we might be on a slippery slope if we start drawing parallels between our work and that work. This might inadvertently contradict the concurrent work policy. So we would prefer for our work to be judged on its own merit. Independent of the above, we agree that the difference between correlation and causation is the most obvious and best known aspect of causality; however,Pearl's framework is much richer than this. While Pearl's framework is not the only one dealing with causality, it is probably the most common one in the ML community, so it is a very natural framework to use here. For example, another approach to causal inference which may be considered, apart from the one based on structural causal models (SCMs), is the potential outcomes (PO) framework. However, while the two approaches dedicate more or less emphasis to different aspects of causal inference, it has been argued that the SCM framework "does not exclude any concept or relationship definable in the PO approach" (see http://causality.cs.ucla.edu/blog/index.php/2020/01/29/on-imbens-comparison-of-two-approaches-to-empirical-economics/). Moreover, the SCM framework was developed by AI researchers (as opposed to the PO framework, which was mostly developed in the context of economics): it therefore puts particular emphasis on algorithmic aspects of causal reasoning (see, e.g., https://ftp.cs.ucla.edu/pub/stat_ser/r360.pdf for counterfactuals). This makes it particularly suited for our objective, where we want to algorithmically generate ground truth answers to causal questions, without having to assess the correctness of an answer based on common sense. We will be happy to elaborate on the aspects above in the Introduction, to better contextualize our work. In case the above does not answer your questions, maybe you could further let us know what you dislike about using the Pearl framework here? Why do you think it would be intellectually preferable not to use it? `Table 2: Can you explain why CausalCoT makes performance drop on Rung 3:` We agree that the result is in some sense counterintuitive: it is an empirical finding, and our explanation is that it might be related to difficulties in distinguishing the query type, as our investigation through CausalCoT shows, since this sub-task is harder for counterfactuals (rung 3) than for lower rungs, due to a broader range of queries to be considered. We can include this in the Limitations section too, in that a precondition for CoT to work well is that each subquestion can be well-addressed by LLMs, which is a bit difficult for the case of the query type questions in Rung 3. And for the significance test, yes we can run a significance test and add it to the next version of the paper. `one important step towards augmenting LLMs is understanding to what extent they already solve certain reasoning tasks.` We would agree that investigating the performance with plugins would be great, and it would be an extremely interesting aspect to investigate in the future. As you suggested, we will discuss this future research direction in the paper. There is, as you mentioned, a thread of work attempting to improve LLM math abilities, e.g., https://arxiv.org/abs/2308.05713, where it is suggested that “the plugins significantly enhance GPT’s ability to solve these problems”. The problem is not solved though: as the authors remark, "there are still often ”interface” failures; that is, GPT often has trouble formulating problems in a way that elicits useful answers from the plug-ins". We expect that something similar will happen for causal inference, once suitable plugins are built, where the language-to-tool interface will still be a non-trivial research question. Moreover, the possible future existence of such plugins will make it even more important to have systematic benchmarks that assay causal inference capabilities in a broad set of tasks (not just correlation vs. causation), so we would view this as nicely complementary to what we do.
Score change
Thank you so much for the detailed replies. **Regarding motivation for Pearl's framework**: I think it is fine to set on that framework, concretely I would suggest expanding the intro a bit in the lines that lead to motivate the problem you decided to study. Regarding Pearl's framework, you could also point out its strengths and cite one or two alternatives if you like. The comparison to Kiciman was intended more to suggest improving the presentation that leads to motivate and *sell* the problem as important to the readers. This could also help improve citation chances as concurrent works will compete to be cited or built upon. **Regarding drop to GPT-4 on Rung 3**: Please do discuss it somewhere, I think it is important to emphasize if one wants to use the technique on Rung 3 problems. I don't think it's necessary to add detailed significance tests, maybe you could point out the ball-park of what type of difference scores are significant (maybe 1 or 2 accuracy points?). **Regarding tools**: Thanks for be willing to discuss it. I will raise the score from 4 to 5. I did not raise it to 6 because of the drop on Rung 3 compared to GPT-4.
Acknowledgement
Thank you very much for raising the score to acceptance, and all your time and efforts engaging in our discussions. We appreciate it.
Dear reviewer, we have read your review carefully, conducted the requested experiment, and included a detailed reply in the rebuttal. Could you let us know if you have further questions? Happy to follow up :)!
Thank you for your rebuttal. I feel relatively positive about the paper as it is the first formal causal inference dataset in NLP, but some concerns still remain, so I decided to keep my original score unchanged. Specifically, 1. The evaluation of the proposed prompting technique is not very thorough. 2. The dataset is large enough (10K) to fine-tune with open-source LLMs. No one will actually evaluate on a 10K dataset. I think some sort of fine-tuning can reveal potential issues of the proposed dataset. e.g. whether there are undiscovered shortcuts, and whether the actual causal reasoning process of this dataset is too easy that the un-fine-tuned models cannot perform well because they rarely saw this causal inference data in the training set, etc. Since training on the proposed dataset to improve the causal reasoning ability of LLMs is also an interesting usage of this dataset, I think it will be very helpful to do the fine-tuning experiments.
Thank you for positively supporting our work. We are glad to take further efforts to address your remaining concerns. **Re the dataset size (10K):** One advantage of our work is that the number of possible questions can be flexibly downsampled or upsampled, so 10K is not a fixed number. Our major contribution is to provide a systematic way (and we also open-source our code) to generate causal inference questions at finger tip and the users can control many possible combinations of graphs, queries etc. So it is also an option to publish a 1K version of the data for the evaluation mode. **Re the potential shortcuts:** We are conducting the finetuning experiments now. It will take some time. In case the results did not get into the rebuttal time window, we can add it in the camera-ready version. Here are some preliminary results: 1. We are training a RoBERTa model now. So far with default parameters, it is still close to random guess with 50% accuracy, and fluctuating a bit between 49% and 51%. We are further finetuning the hyperparameters to see if it achieves higher accuracy. 2. In the meantime, we did a search over spurious correlations by calculating how correlated are the ngrams to labels. We report the absolute value of the [normalized point-wise mutual information](https://en.wikipedia.org/wiki/Pointwise_mutual_information#Normalized_pointwise_mutual_information_(npmi)) (nPMI) between each ngram and the label. Note that the value of nPMI is between [-1,+1], where -1 (in the limit) is never occurring together, 0 is independence, and +1 is complete co-occurrence. - 1-gram (single word): average |nPMI|=0.005 - 2-gram (two-word combination): average |nPMI|=0.007 - 3-gram: average |nPMI|=0.011 All of the above |nPMI|s are pretty small, close to 0, which means that they are largely independent, which also echoes with the design of our dataset that balances our data generation process (incl., causal graphs, query types, etc) across the positive and negative labels. We are happy to add these above results to the future version of the paper to show the quality of our dataset. **Re “the evaluation of the proposed prompting technique is not very thorough.”:** Thank you for the note. In the paper, we provide detailed analysis of the prompting technique, which we can highlight some main findings below: - In Section 5.4 Error Analysis by Subquestions, we can see in Table 3 the performance of each single step. For example, the models deal well with the arithmetics, and causal graph extraction (which are common tasks that LLMs have been more or less trained on), while the Step 2 query type classification is very difficult, which is a crucial contribution of the causal inference community, and a highly technical skill. Our dataset provides a first step to introduce the formal causal inference task to LLMs. - In Section 5.5, as well as our [additional experiment](https://openreview.net/forum?id=e2wtjx0Yqu¬eId=NLKhXRNjHH) to Reviewer rbbm, we can see the effect of each step on the final accuracy. For example, Step 2 and 3, which are crucial for the formalization of the estimated, contributes the most to the CoT process. Does the above address your concerns? Feel free to let us know if you need any additional information.
Decision
Accept (poster)