• zip file size: 6.5MB
• Version: v1
• Date: 2023-05-25
• Huggingface dataset: https://huggingface.co/datasets/causalnlp/CLadder

This repo contains the full CLadder dataset (and code) for evaluating (formal) causal reasoning in language models. The dataset asks yes/no questions in natural language that generally require statistical and causal inference to answer.

Although there are several different variants, the main dataset (including questions from all variants) is cladder-v1-balanced.json, so that is the recommended file to use for most purposes.

"CLadder: Assessing Causal Reasoning in Language Models" by Zhijing Jin*, Yuen Chen*, Felix Leeb*, Luigi Gresele*, Ojasv Kamal, Zhiheng Lyu, Kevin Blin, Fernando Gonzalez, Max Kleiman-Weiner, Mrinmaya Sachan, Bernhard Schölkopf.

Citation:

@inproceedings{jin2023cladder,
    author = {Zhijing Jin and Yuen Chen and Felix Leeb and Luigi Gresele and Ojasv Kamal and Zhiheng Lyu and Kevin Blin and Fernando Gonzalez and Max Kleiman-Weiner and Mrinmaya Sachan and Bernhard Sch{\"{o}}lkopf},
    title = "{CL}adder: {A}ssessing Causal Reasoning in Language Models",
    year = "2023",
    booktitle = "NeurIPS",
    url = "https://openreview.net/forum?id=e2wtjx0Yqu",
}

You can download our data either from huggingface (https://huggingface.co/datasets/causalnlp/CLadder), or cladder-v1.zip in our repo.

In our data, each sample represents a single question. Each question has the following fields:

• question_id: a unique (for the file) identifier for the question
• desc_id: a more descriptive identifier for the question (generally not needed)
• given_info: natural language supplementary information that should be given to the model to answer the question.
• question: the question itself, in natural language
• answer: the answer to the question {yes, no}
• reasoning: a step-by-step explanation of the causal reasoning used to answer the question
• meta: metadata about the question, including the following fields:
  • query_type: the type of question, one of {ATE, marginal, correlation, ETT, NDE, NIE, etc.}
  • rung: the rung of the ladder of causation that the question corresponds to
  • story_id: the id of the story used to verbalize the question
  • graph_id: the id of the causal graph structure used to verbalize the question
  • model_id: the id of the underlying model used to generate the question (corresponding to a model in cladder-v1-meta-models.json)
  • groundtruth: the groundtruth value of what the question is asking about

When evaluating a language model, it is recommended that the prompt includes 3 components:

1. The background field of the model corresponding to the question (found in cladder-v1-meta-models.json using the model_id field of the question's metadata).
2. The given_info field of the question.
3. The question field of the question.

For example, the prompt corresponding to question 16825 (which asks about the average treatment effect for a simple instrumental variable setting) in cladder-v1-balanced.json could be:

Imagine a self-contained, hypothetical world with only the following conditions, and without any unmentioned factors or causal relationships: Unobserved confounders has a direct effect on education level and salary. Proximity to a college has a direct effect on education level. Education level has a direct effect on salary. Unobserved confounders is unobserved.

For people living far from a college, the probability of high salary is 35%. For people living close to a college, the probability of high salary is 53%. For people living far from a college, the probability of college degree or higher is 40%. For people living close to a college, the probability of college degree or higher is 73%.

Will college degree or higher decrease the chance of high salary?

Here the correct answer is no. The associated reasoning steps found in the reasoning field are:

Step 0: Let V2 = proximity to a college; V1 = unobserved confounders; X = education level; Y = salary.

Step 1: V1->X,V2->X,V1->Y,X->Y

Step 2: E[Y | do(X = 1)] - E[Y | do(X = 0)]

Step 3: [P(Y=1|V2=1)-P(Y=1|V2=0)]/[P(X=1|V2=1)-P(X=1|V2=0)]

Step 4: P(Y=1 | V2=0) = 0.35; P(Y=1 | V2=1) = 0.53; P(X=1 | V2=0) = 0.40; P(X=1 | V2=1) = 0.73

Step 5: (0.53 - 0.35) / (0.73 - 0.40) = 0.55

Solution: 0.55 > 0

Note that in addition to the background field, the model information found in cladder-v1-meta-models.json contains sufficient information to fully reconstruct the underlying causal model used to generate this question (and 59 others).

Here are some basic statistics for the main dataset (cladder-v1-balanced.json).

Number of questions: 10,112 Answers: {"yes": 5,056, "no": 5,056}

Query Types:

Graph Types:

If you want to dig a little deeper into understanding how well language models perform causal reasoning, we also include a few variants of the dataset (each of which contains about 10k questions, and the balanced dataset is made up of an even mix of these variants):

• cladder-v1-aggregate.json: a combination of all the variants below but where each story has approximately the same number of questions (100-200).
• cladder-v1-q-easy.json: questions that are easy to answer (i.e. the causal mechanisms generally conform to what you would expect)
• cladder-v1-q-hard.json: the structure of the causal graph remains unchanged, but the strengths of causal mechanisms are generally counterintuitive
• cladder-v1-q-commonsense.json: an even mix of easy and hard questions
• cladder-v1-q-anticommonsense.json: for each causal graph we replace one of the variables (either treatment or outcome) with a randomly selected one that common sense would tell you is not related to the other variable at all.
• cladder-v1-q-nonsense.json: here the graph structure remains unchanged, but all variables are replaced from semantically meaningful concepts to randomly generated 4-letter words.

To use the codes in this repo, first clone this repo:

git clone https://github.com/causalNLP/causalbenchmark
cd causalbenchmark

Then, install the dependencies:

pip install -r requirements.txt

Finally, install the package:

pip install -e .

Check to make sure everything is setup correctly by running the unit tests:

pytest

Generate demo data using

fig generate demo

Checkout the corresponding config file here.

And the script which is implemented in generator.py - the function generate_and_store.

Also, you can run the unit tests with

pytest

Check the eval/ folder for all the run_*.py code files in to see how to run different LLMs in inference mode on our data.

We saved a copy of all model output files, which you can access here.

Thanks again for your interest in our work! Feel free to post a github issue if you have any questions.