diff --git a/dowhy/causal_estimators/causalml.py b/dowhy/causal_estimators/causalml.py
index d44f0d9ac8..9b3b554562 100644
--- a/dowhy/causal_estimators/causalml.py
+++ b/dowhy/causal_estimators/causalml.py
@@ -11,11 +11,9 @@
 
 
 class _CausalmlEstimator(Protocol):
-    def estimate_ate(self, *args, **kwargs):
-        ...
+    def estimate_ate(self, *args, **kwargs): ...
 
-    def fit_predict(self, *args, **kwargs):
-        ...
+    def fit_predict(self, *args, **kwargs): ...
 
 
 logger = logging.getLogger(__name__)
diff --git a/dowhy/causal_estimators/econml.py b/dowhy/causal_estimators/econml.py
index 33cd4b0b82..98845b7375 100755
--- a/dowhy/causal_estimators/econml.py
+++ b/dowhy/causal_estimators/econml.py
@@ -13,20 +13,15 @@
 
 
 class _EconmlEstimator(Protocol):
-    def fit(self, *args, **kwargs):
-        ...
+    def fit(self, *args, **kwargs): ...
 
-    def effect(self, *args, **kwargs):
-        ...
+    def effect(self, *args, **kwargs): ...
 
-    def effect_interval(self, *args, **kwargs):
-        ...
+    def effect_interval(self, *args, **kwargs): ...
 
-    def effect_inference(self, *args, **kwargs):
-        ...
+    def effect_inference(self, *args, **kwargs): ...
 
-    def shap_values(self, *args, **kwargs):
-        ...
+    def shap_values(self, *args, **kwargs): ...
 
 
 class Econml(CausalEstimator):
diff --git a/dowhy/causal_graph.py b/dowhy/causal_graph.py
index 1290430c68..098be0af95 100755
--- a/dowhy/causal_graph.py
+++ b/dowhy/causal_graph.py
@@ -11,7 +11,6 @@
 
 
 class CausalGraph:
-
     """Class for creating and modifying the causal graph.
 
     Accepts a networkx DiGraph, a :py:class:`ProbabilisticCausalModel <dowhy.gcm.ProbabilisticCausalModel`, a graph string (or a text file) in gml format (preferred) or dot format. Graphviz-like attributes can be set for edges and nodes. E.g. style="dashed" as an edge attribute ensures that the edge is drawn with a dashed line.
diff --git a/dowhy/causal_identifier/identified_estimand.py b/dowhy/causal_identifier/identified_estimand.py
index 6e802980da..bb6f2e9a05 100644
--- a/dowhy/causal_identifier/identified_estimand.py
+++ b/dowhy/causal_identifier/identified_estimand.py
@@ -7,7 +7,6 @@
 
 
 class IdentifiedEstimand:
-
     """Class for storing a causal estimand, typically as a result of the identification step."""
 
     def __init__(
diff --git a/dowhy/causal_model.py b/dowhy/causal_model.py
index 8321a8ac1e..d1c3c17884 100755
--- a/dowhy/causal_model.py
+++ b/dowhy/causal_model.py
@@ -1,6 +1,7 @@
 """ Module containing the main model class for the dowhy package.
 
 """
+
 import logging
 import typing
 import warnings
diff --git a/dowhy/causal_refuter.py b/dowhy/causal_refuter.py
index 45f9c293ed..cd09481ca0 100644
--- a/dowhy/causal_refuter.py
+++ b/dowhy/causal_refuter.py
@@ -20,7 +20,6 @@ class SignificanceTestType(Enum):
 
 
 class CausalRefuter:
-
     """Base class for different refutation methods.
 
     Subclasses implement specific refutations methods.
diff --git a/dowhy/causal_refuters/add_unobserved_common_cause.py b/dowhy/causal_refuters/add_unobserved_common_cause.py
index 80f0ef18f2..e68d33f2ee 100755
--- a/dowhy/causal_refuters/add_unobserved_common_cause.py
+++ b/dowhy/causal_refuters/add_unobserved_common_cause.py
@@ -29,7 +29,6 @@
 
 
 class AddUnobservedCommonCause(CausalRefuter):
-
     """Add an unobserved confounder for refutation.
 
     AddUnobservedCommonCause class supports three methods:
diff --git a/dowhy/causal_refuters/overrule/ruleset.py b/dowhy/causal_refuters/overrule/ruleset.py
index 82957412c3..ab328ca315 100644
--- a/dowhy/causal_refuters/overrule/ruleset.py
+++ b/dowhy/causal_refuters/overrule/ruleset.py
@@ -7,6 +7,7 @@
 Overlap in Observational Studies. In S. Chiappa & R. Calandra (Eds.), Proceedings of the Twenty Third International
 Conference on Artificial Intelligence and Statistics (Vol. 108, pp. 788–798). PMLR. https://arxiv.org/abs/1907.04138
 """
+
 from typing import Callable, Dict, List, Optional
 
 import numpy as np
diff --git a/dowhy/gcm/auto.py b/dowhy/gcm/auto.py
index 2fd4d20c62..1ff8587340 100644
--- a/dowhy/gcm/auto.py
+++ b/dowhy/gcm/auto.py
@@ -157,7 +157,7 @@ def __str__(self):
                     % self._nodes[node]["model_performances"][0][2]
                 )
 
-                for (model, performance, metric_name) in self._nodes[node]["model_performances"]:
+                for model, performance, metric_name in self._nodes[node]["model_performances"]:
                     summary_strings.append("%s: %s" % (str(model()).replace("()", ""), str(performance)))
 
         summary_strings.append(
@@ -344,7 +344,7 @@ def assign_causal_mechanisms(
                 + ",N).",
             )
 
-        for (model, performance, metric_name) in model_performances:
+        for model, performance, metric_name in model_performances:
             auto_assignment_summary.add_model_performance(node, model, performance, metric_name)
 
     return auto_assignment_summary
diff --git a/dowhy/gcm/falsify.py b/dowhy/gcm/falsify.py
index 704fff2f78..7f42970dff 100644
--- a/dowhy/gcm/falsify.py
+++ b/dowhy/gcm/falsify.py
@@ -1,4 +1,5 @@
 """This module provides functionality to falsify a user-given DAG given observed data."""
+
 import warnings
 from dataclasses import dataclass, field
 from enum import Enum, auto
diff --git a/dowhy/gcm/feature_relevance.py b/dowhy/gcm/feature_relevance.py
index 80afd3f182..bcbcd7e41a 100644
--- a/dowhy/gcm/feature_relevance.py
+++ b/dowhy/gcm/feature_relevance.py
@@ -2,6 +2,7 @@
 be blackbox prediction models, it is also possible to explain causal mechanisms with respect to the direct parents.
 In these cases, it would be possible to incorporate the noise to represent the part of the generation process that
 cannot be explained by the parents."""
+
 from typing import Any, Callable, Dict, Optional, Tuple, Union
 
 import numpy as np
@@ -258,9 +259,9 @@ def single_sample_set_function(subset: np.ndarray) -> Union[np.ndarray, float]:
             baseline_samples=baseline_samples,
             baseline_feature_indices=np.arange(0, feature_samples.shape[1])[subset == 1],
             return_averaged_results=False,
-            feature_perturbation="randomize_columns_jointly"
-            if randomize_features_jointly
-            else "randomize_columns_independently",
+            feature_perturbation=(
+                "randomize_columns_jointly" if randomize_features_jointly else "randomize_columns_independently"
+            ),
             max_batch_size=max_batch_size,
         )
 
diff --git a/dowhy/gcm/independence_test/regression.py b/dowhy/gcm/independence_test/regression.py
index 220c6a5a52..f10e1aaa4b 100644
--- a/dowhy/gcm/independence_test/regression.py
+++ b/dowhy/gcm/independence_test/regression.py
@@ -1,6 +1,7 @@
 """ Regression based (conditional) independence test. Testing independence via regression, i.e. if a variable has
 information about another variable, then they are dependent.
 """
+
 from typing import Callable, List, Optional, Union
 
 import numpy as np
diff --git a/dowhy/gcm/influence.py b/dowhy/gcm/influence.py
index b8988e5e8a..4934007f01 100644
--- a/dowhy/gcm/influence.py
+++ b/dowhy/gcm/influence.py
@@ -1,4 +1,5 @@
 """This module provides functions to estimate causal influences."""
+
 import logging
 import warnings
 from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast
diff --git a/dowhy/gcm/shapley.py b/dowhy/gcm/shapley.py
index 89345164c6..29c6602459 100644
--- a/dowhy/gcm/shapley.py
+++ b/dowhy/gcm/shapley.py
@@ -592,7 +592,7 @@ def parallel_job(input_subset: Tuple[int], parallel_random_seed: int) -> Union[f
     )
 
     subset_to_result_map = {}
-    for (subset, result) in zip(evaluation_subsets, subset_results):
+    for subset, result in zip(evaluation_subsets, subset_results):
         subset_to_result_map[subset] = result
 
     return subset_to_result_map
diff --git a/dowhy/gcm/validation.py b/dowhy/gcm/validation.py
index 6418e735e7..f0fe890d65 100644
--- a/dowhy/gcm/validation.py
+++ b/dowhy/gcm/validation.py
@@ -1,4 +1,5 @@
 """Contains a method to reject the causal graph and validate causal mechanisms such as post non-linear models."""
+
 from enum import Enum, auto
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
diff --git a/dowhy/graph.py b/dowhy/graph.py
index 805c940ca7..b33f5933be 100644
--- a/dowhy/graph.py
+++ b/dowhy/graph.py
@@ -1,4 +1,5 @@
 """This module defines the fundamental interfaces and functions related to causal graphs."""
+
 import itertools
 import logging
 import re
diff --git a/dowhy/interpreters/confounder_distribution_interpreter.py b/dowhy/interpreters/confounder_distribution_interpreter.py
index eac67166bd..575c2de24a 100644
--- a/dowhy/interpreters/confounder_distribution_interpreter.py
+++ b/dowhy/interpreters/confounder_distribution_interpreter.py
@@ -64,7 +64,6 @@ def discrete_dist_plot(labels, not_treated_counts, treated_counts, ax, title, va
         ax.legend()
 
     def interpret(self, data: pd.DataFrame):
-
         """
         Shows distribution changes for confounding variables before and after applying inverse propensity weights.
         """
diff --git a/dowhy/utils/dgps/linear_dgp.py b/dowhy/utils/dgps/linear_dgp.py
index e78261f29c..da5411da3f 100644
--- a/dowhy/utils/dgps/linear_dgp.py
+++ b/dowhy/utils/dgps/linear_dgp.py
@@ -63,18 +63,10 @@ def generate_data(self, sample_size):
     def generation_process(self):
         self.weights["confounder=>treatment"] = self.generate_weights((len(self.confounder), len(self.treatment)))
         self.weights["confounder=>treatment"][0,] = (
-            self.weights["confounder=>treatment"][
-                0,
-            ]
-            + 100
+            self.weights["confounder=>treatment"][0,] + 100
         )  # increasing weight of the first confounder
         self.weights["confounder=>outcome"] = self.generate_weights((len(self.confounder), len(self.outcome)))
-        self.weights["confounder=>outcome"][0,] = (
-            self.weights["confounder=>outcome"][
-                0,
-            ]
-            + 100
-        )
+        self.weights["confounder=>outcome"][0,] = self.weights["confounder=>outcome"][0,] + 100
         self.weights["effect_modifier=>outcome"] = self.generate_weights((len(self.effect_modifier), len(self.outcome)))
         self.weights["treatment=>outcome"] = self.generate_weights((len(self.treatment), len(self.outcome)))
 
diff --git a/dowhy/utils/graphviz_plotting.py b/dowhy/utils/graphviz_plotting.py
index 7eec932d7b..d686d34860 100644
--- a/dowhy/utils/graphviz_plotting.py
+++ b/dowhy/utils/graphviz_plotting.py
@@ -30,7 +30,7 @@ def plot_causal_graph_graphviz(
         layout_prog = "dot"
 
     max_strength = 0.0
-    for (source, target, strength) in causal_graph.edges(data="CAUSAL_STRENGTH", default=None):
+    for source, target, strength in causal_graph.edges(data="CAUSAL_STRENGTH", default=None):
         if (source, target) not in causal_strengths:
             causal_strengths[(source, target)] = strength
         if causal_strengths[(source, target)] is not None:
@@ -46,7 +46,7 @@ def plot_causal_graph_graphviz(
         else:
             pygraphviz_graph.add_node(node)
 
-    for (source, target) in causal_graph.edges():
+    for source, target in causal_graph.edges():
         causal_strength = causal_strengths[(source, target)]
         color = colors[(source, target)]
         if causal_strength is not None:
diff --git a/dowhy/utils/networkx_plotting.py b/dowhy/utils/networkx_plotting.py
index 06f97c89d9..34aed65cbf 100644
--- a/dowhy/utils/networkx_plotting.py
+++ b/dowhy/utils/networkx_plotting.py
@@ -28,7 +28,7 @@ def plot_causal_graph_networkx(
         colors = deepcopy(colors)
 
     max_strength = 0.0
-    for (source, target, strength) in causal_graph.edges(data="CAUSAL_STRENGTH", default=None):
+    for source, target, strength in causal_graph.edges(data="CAUSAL_STRENGTH", default=None):
         if (source, target) not in causal_strengths:
             causal_strengths[(source, target)] = strength
 
diff --git a/poetry.lock b/poetry.lock
index ed0d4073cf..a34c22011b 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -364,38 +364,49 @@ lxml = ["lxml"]
 
 [[package]]
 name = "black"
-version = "22.12.0"
+version = "24.3.0"
 description = "The uncompromising code formatter."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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-    {file = "black-22.12.0-cp310-cp310-win_amd64.whl", hash = "sha256:159a46a4947f73387b4d83e87ea006dbb2337eab6c879620a3ba52699b1f4351"},
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+    {file = "black-24.3.0.tar.gz", hash = "sha256:a0c9c4a0771afc6919578cec71ce82a3e31e054904e7197deacbc9382671c41f"},
 ]
 
 [package.dependencies]
 click = ">=8.0.0"
 ipython = {version = ">=7.8.0", optional = true, markers = "extra == \"jupyter\""}
 mypy-extensions = ">=0.4.3"
+packaging = ">=22.0"
 pathspec = ">=0.9.0"
 platformdirs = ">=2"
 tokenize-rt = {version = ">=3.2.0", optional = true, markers = "extra == \"jupyter\""}
-tomli = {version = ">=1.1.0", markers = "python_full_version < \"3.11.0a7\""}
-typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 colorama = ["colorama (>=0.4.3)"]
-d = ["aiohttp (>=3.7.4)"]
+d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
@@ -1905,7 +1916,6 @@ optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
 files = [
     {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
-    {file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"},
 ]
 
 [[package]]
@@ -2348,8 +2358,6 @@ description = "Clang Python Bindings, mirrored from the official LLVM repo: http
 optional = false
 python-versions = "*"
 files = [
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@@ -5075,8 +5054,8 @@ files = [
 
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     {version = ">=1.18", markers = "python_version != \"3.10\" or platform_system != \"Windows\" or platform_python_implementation == \"PyPy\""},
+    {version = ">=1.22.3", markers = "python_version == \"3.10\" and platform_system == \"Windows\" and platform_python_implementation != \"PyPy\""},
 ]
 packaging = ">=21.3"
 pandas = ">=1.0"
@@ -5972,4 +5951,4 @@ pygraphviz = ["pygraphviz"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8,<3.12"
-content-hash = "c5222c089b2dfa6c17953dc1c36143a951c4a20ae367f6eb2f340d05a699159c"
+content-hash = "3039466778b222a3e6124bc1ebc3a6d94cbaff5ece3f641ad453f303e619dca0"
diff --git a/pyproject.toml b/pyproject.toml
index 12e1685201..e82002212c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -82,7 +82,7 @@ econml = ["econml"]
 [tool.poetry.group.dev.dependencies]
 poethepoet = "^0.24.4"
 flake8 = "^5.0.4"
-black = { version = "^22.6.0", extras = ["jupyter"] }
+black = { version = ">=22.6,<25.0", extras = ["jupyter"] }
 isort = "^5.10.1"
 pytest = "^7.1.2"
 pytest-cov = "^4.1.0"