Comments (1)
Are those 1000 samples individual units e.g. patients for example?
Yes
Can we also generate multiple samples for a unit, for example this can be a treatement test over many days to measure the response. I want to then be able to say: 10 units x 10 days = 100 samples.
This is not supported. Will be great if you can add such a dataset simulator.
Why the ate, att, atc are identical?
This is because the true effect is a linear effect. It is homogeneous on the entire population. So it does not matter whether you compute causal effect on everyone, only on the treated, or only on the untreated. It is the same effect.
With a different simulated dataset, these quantities will be different.
Since the W0 treatment is continuous how the system knows to discriminate between the treated and untreated?
Treatment is v0. W0 is a confounder. For linear treatments, user has to specify the "treatment" and control" values (usually 1 and 0 respectively).
How can we constraint the generation for example I want to have only treatment and outcomes in the positive range between 0 and 100.
Not possible with the current function. You will need to add a new function or modify this one.
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Related Issues (20)
- Python 3.12 support HOT 9
- Clarify the differences among refute methods HOT 11
- Feature relevance/Influence HOT 26
- Graphviz installation : --include-path not recognized anymore HOT 4
- Does this package support non-English languages? HOT 3
- Question about Dummy Outcome Refuter HOT 2
- Inconsistency in the placebo_treatment_refuter when using estimate_effect of IV HOT 1
- numpy.dual is dropped but it still occurs in dowhy HOT 2
- NetworkXError: graph should be directed acyclic HOT 4
- Refutation & Overlap Error ("data_subset_refuter", "add_unobserved_common_cause", assess_support_and_overlap_overrule) HOT 2
- No Backdoor Path Available
- Clarification on how to use gcm properly for confounders adjustment HOT 5
- Can you provide code demo for each function? HOT 2
- How is propensity score matching implemented? HOT 2
- Interpreting mean while using logistic regression to estimate causal effect. HOT 1
- model.estimate_effect and model.refute_astimate throws 'A column-vector y was passed ...' error
- RuntimeWarning: divide by zero encountered in divide when using evaluate_causal_model HOT 3
- Auto assign_causal_mechanisms is taking so much time in gcm HOT 11
- falsify_graph HOT 8
- Remove use of CausalModel from test files and notebooks
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